Disease prediction system and disease prediction method

Abstract

The purpose of the present invention is to provide a disease prediction system and a disease prediction method which are for animals. This disease prediction system is characterized by comprising a prediction unit for predicting whether an animal will suffer from a disease within a predetermined period, by using information pertaining to halitosis of the animal. It is preferable that the prediction unit predicts whether an animal will suffer from a disease within a predetermined period by using information pertaining to halitosis of the animal and information pertaining to intestinal bacteria of the animal.

Description

Disease prediction system and disease prediction method

[0001] The present invention relates to a disease prediction system and a disease prediction method, and more particularly to a disease prediction system and a disease prediction method that provide information on the future likelihood of an animal contracting a disease from information on the animal's bad breath, or information on the bad breath and intestinal bacteria.

[0002] Pets such as dogs, cats, and rabbits, as well as livestock such as cows and pigs, are irreplaceable to humans. While the average lifespan of animals kept by humans has increased significantly in recent years, the number of animals suffering from illnesses during their lives has also increased, resulting in an increase in medical expenses borne by owners.

[0003] In order to maintain the health of animals, it is important to manage their physical condition through daily diet and exercise, and to respond quickly to any illnesses. However, because animals cannot verbally express their physical ailments, the reality is that caretakers only become aware that their animals are suffering from an illness when the symptoms progress and some outwardly observable signs appear.

[0004] Therefore, there is a need for a simple method for determining whether an animal is likely to contract a disease in the future. In particular, if it were possible to determine the possibility of future disease contraction in an animal that is not infected with a disease or has no symptoms, it would be useful because it would enable specific measures to be taken to prevent the disease.

[0005] Patent Document 1 discloses an intestinal flora adjusting or improving composition that has the effect of effectively adjusting or improving the intestinal flora by increasing bacteria of the Bacteroidetes phylum and reducing bacteria of the Firmicutes phylum in the intestinal flora, but does not disclose a method for predicting whether an animal will become ill based on data regarding the animal's intestinal flora.

[0006] Furthermore, Patent Document 2 describes a risk assessment system that assesses a patient's risk of systemic disease based on risk information about the oral environment, such as information about the patient's level of bad breath. However, this system is intended for humans and requires tests such as periodontal pocket examinations, oral specimen examinations, or nucleic acid amplification tests for periodontal disease bacteria.

[0007] International Publication No. 2017 / 094892 Pamphlet Japanese Patent Application Laid-Open No. 2023-159721

[0008] Therefore, an object of the present invention is to provide a disease prediction system or the like that can predict the possibility that animals other than humans will contract a disease in the future using a simple method.

[0009] The present inventors analyzed and examined a huge amount of data on information about the bad breath of animals enrolled in pet insurance and whether or not the animals have filed insurance claims, i.e., whether or not the animals have contracted a disease, and as a result discovered that it is possible to predict whether or not the animals will contract a disease in the future using information about the bad breath of the animals, leading to the completion of the present invention. Furthermore, the present inventors also discovered that by combining information about the bad breath of the animals with information about the intestinal bacteria of the animals, it is possible to more accurately predict whether or not the animals will contract a disease.

[0010] That is, the present invention encompasses the following [1] to

[15] . [1] A disease prediction system comprising a prediction unit that uses information about an animal's halitosis to predict whether the animal will contract a disease within a predetermined period of time or whether the animal currently has a disease. [2] The disease prediction system of [1], further comprising a proposal unit that proposes measures to reduce the possibility of contracting a disease. [3] The disease prediction system of [1], in which the information about the animal's halitosis is a questionnaire about the presence or absence of halitosis obtained from the animal's owner or manager, or the results of odor measurement using an odor determination device. [4] The disease prediction system of [1], in which the information about the animal's halitosis is information about the presence or absence of halitosis at different dates and times obtained during a predetermined period of time. [5] The disease prediction system of [1], in which the prediction unit uses information about the animal's halitosis and information about the animal's intestinal bacteria to predict whether the animal will contract a disease within a predetermined period of time or whether the animal currently has a disease. [6] The disease prediction system of [5], in which the information about the animal's intestinal bacteria is information about the diversity of the intestinal microflora. [7] The disease prediction system of [5], wherein the time when the information about the animal's halitosis information and the time when the information about the animal's intestinal bacteria are acquired are not separated by more than 180 days. [8] The disease prediction system of [5], wherein the information about the animal's intestinal bacteria is stored in a database, and when the acquisition unit acquires information about the animal's halitosis, information about the intestinal bacteria of the same individual animal is retrieved from the database, and the prediction unit uses the information about the animal's halitosis and the information about the animal's intestinal bacteria to predict whether the animal will contract a disease within a specified period of time or whether it is currently contracting a disease. [9] A disease prediction method for an animal, wherein a computer uses information about the animal's halitosis to predict whether the animal will contract a disease within a specified period of time or whether it is currently contracting a disease.

[10] A health condition prediction system comprising an acquisition unit that acquires halitosis information about the animal's halitosis, and a health condition prediction unit that uses the information about the halitosis to predict the animal's health condition or future health condition.

[11] The health condition prediction system of

[10] , wherein the health condition prediction unit predicts the health condition or future health condition of the animal using information about the animal's halitosis and information about the animal's intestinal bacteria.

[12] The health condition prediction system of

[11] , wherein the information about the animal's intestinal bacteria is information about the diversity of the intestinal microflora.

[13] A health condition prediction program that causes a computer to execute the steps of acquiring halitosis information about the animal's halitosis and predicting the health condition or future health condition of the animal using the information about the halitosis.

[14] An information processing system comprising an acquisition unit that acquires information about the animal's halitosis and an output unit that outputs information encouraging care of the animal if the animal has halitosis based on the halitosis information.

[15] An information processing program that causes a computer to execute the steps of acquiring information about the animal's halitosis and encouraging care of the animal if the animal has halitosis based on the halitosis information.

[0011] The present invention makes it possible to provide a disease prediction system and a disease prediction method for predicting the likelihood that an animal will contract a disease in the future.

[0012] FIG. 1 is a schematic diagram of a disease prediction system of the present invention. FIG. 2 is a graph showing the relationship between the number of types of food and the diversity of intestinal bacteria. FIG. 3 is a flow diagram of a disease prediction method of the present invention. FIG. 4 is a graph showing the results of a reference example. FIG. 5 is a graph showing the results of a reference example. FIG. 6 is a graph showing the results of a reference example. FIG. 7 is a graph showing the results of a reference example. FIG. 8 is a graph showing the results of a reference example. FIG. 9 is a graph showing the results of a reference example. FIG. 10 is a graph showing the results of a reference example. FIG. 11 is a graph showing the results of a reference example. FIG. 12 is a graph showing the results of a reference example. FIG. 13 is a graph showing the results of a reference example. FIG. 14 is a graph showing the results of a reference example. FIG. 15 is a graph showing the results of a reference example. FIG. 16 is a graph showing the results of a reference example. FIG. 17 is a graph showing the results of a reference example. Graph showing the results of a reference example (hadoritis in dogs and prevalence of digestive system diseases). Graph showing the results of a reference example (hadoritis in dogs and prevalence of blood and hematopoietic diseases). Graph showing the results of a reference example (hadoritis in dogs and prevalence of blood and hematopoietic tumors). Graph showing the results of a reference example (hadoritis in dogs and prevalence of nervous system diseases). Graph showing the results of a reference example (hadoritis in dogs and prevalence of epilepsy). Graph showing the results of a reference example (hadoritis in dogs and prevalence of brain tumors). Graph showing the results of a reference example (hadoritis in dogs and prevalence of endocrine system diseases). Graph showing the results of a reference example (hadoritis in dogs and prevalence of diabetes). Graph showing the results of a reference example (hadoritis in dogs and prevalence of uveitis). Graph showing the results of a reference example (hadoritis in dogs and prevalence of lethargy in dogs). Graph showing the results of a reference example (hadoritis in dogs and mortality rate). FIG. 1 is a graph showing the results of a reference example (rate of dogs being bothered by bad breath and fur gloss); FIG. 2 is a graph showing the results of a reference example (rate of dogs being shy around strangers); FIG. 3 is a graph showing the results of a reference example (rate of dogs being shy around animals); FIG. 4 is a graph showing the results of a reference example (rate of dogs being shy around animals); FIG. 5 is a graph showing the results of a reference example (rate of dogs being shy around skin tumors and bad breath); FIG. 6 is a graph showing the results of a reference example (rate of cats being shy around cats and periodontal disease).FIG. 1 is a graph showing the results of a reference example (cat halitosis and prevalence of stomatitis). FIG. 2 is a graph showing the results of a reference example (cat halitosis and prevalence of oral tumors). FIG. 3 is a graph showing the results of a reference example (cat halitosis and prevalence of digestive system diseases). FIG. 4 is a graph showing the results of a reference example (cat halitosis and prevalence of gastritis, gastroenteritis, and enteritis). FIG. 5 is a graph showing the results of a reference example (cat halitosis and prevalence of circulatory system diseases). FIG. 6 is a graph showing the results of a reference example (cat halitosis and prevalence of valvular disease). FIG. 7 is a graph showing the results of a reference example (cat halitosis and prevalence of chronic kidney disease). FIG. 8 is a graph showing the results of a reference example (cat halitosis and prevalence of atopic dermatitis). FIG. 9 is a graph showing the results of a reference example (cat halitosis and prevalence of allergic dermatitis). FIG. 10 is a graph showing the results of a reference example (cat halitosis and prevalence of diabetes). FIG. 1 is a graph showing the results of a reference example (cat halitosis and the prevalence of neoplastic diseases). FIG. 2 is a graph showing the results of a reference example (cat halitosis and the prevalence of blood and immune system diseases). FIG. 3 is a graph showing the results of a reference example (cat halitosis and the prevalence of blood and hematopoietic tumors). FIG. 4 is a graph showing the results of a reference example (cat halitosis and mortality). FIG. 5 is a graph showing the results of a reference example (cat halitosis and the rate at which cats are bothered by their fur gloss). FIG. 6 is a graph showing the results of a reference example (cat halitosis and the rate at which cats are shy around people). FIG. 7 is a graph showing the results of a reference example (cat halitosis and the rate at which cats are shy around animals).

[0013] [Disease Prediction System] The disease prediction system of the present invention is characterized by comprising a prediction unit that uses information about an animal's halitosis to predict whether the animal will develop a disease within a predetermined period of time. Examples of animals include dogs, cats, birds, rabbits, ferrets, and meerkats. Preferably, the disease prediction system of the present invention is configured such that the prediction unit uses information about the animal's halitosis and information about the animal's intestinal bacteria to predict whether the animal will develop a disease within a predetermined period of time. A comprehensive or specific aspect of the disease prediction system of the present invention may be realized as a system, device, server, method, integrated circuit, computer program, or storage medium, or may be realized as any combination of a system, device, method, integrated circuit, computer program, server, or storage medium. Furthermore, as long as the disease prediction system of the present invention has the above-described configuration, it may be combined with other systems or terminals, such as a veterinary clinic's medical record system, a pet insurance system, or a server for an app for pet insurance subscribers, or may be connected via a network such as the Internet.

[0014] [Prediction Unit] The prediction unit of the present invention is a means for predicting whether an animal will develop a disease within a predetermined period of time or whether it currently has a disease based on information about the animal's bad breath. The prediction unit is composed of, for example, a processing device such as a CPU, and calculates the disease susceptibility using a prediction and judgment program, a trained model, software including the trained model, or the program. The program, trained model, or software including the trained model may be stored in a separate storage device. The prediction and judgment method used by the prediction unit is not particularly limited. For example, a processor uses a preset program to predict and judge whether an animal will develop a disease within a predetermined period of time or whether it currently has a disease based on information about the animal's bad breath. Alternatively, the system may be configured to calculate a score based on information about the presence or severity of bad breath according to preset criteria, and determine the disease risk based on a total score obtained by adding up the scores. That is, if the total score is equal to or greater than a predetermined value, the disease risk is high, and if the total score is less than the predetermined value, the disease risk is low. When calculating the score, in addition to information and data related to bad breath, basic information about the animal, such as age, breed, sex, medical history, weight, etc., can also be used.

[0015] The prediction unit of the present invention uses information about the animal's halitosis to predict whether the animal will develop a disease, whether an asymptomatic disease will become apparent, or whether the animal is currently suffering from a disease, preferably within a predetermined period, more preferably within a predetermined period from the time of reception, the time of halitosis collection, or the time of determining the presence or severity of halitosis. The predetermined period is preferably within three years, more preferably within two years, even more preferably within one year, and particularly preferably within 180 days.

[0016] The information about halitosis is information or data related to the strength of halitosis, and may be either a subjective assessment or an objective numerical value. Preferably, it is the result of a questionnaire regarding the presence or strength of halitosis obtained from the animal's owner or manager, or the result of an odor measurement using an odor assessment device. It may also be the result of a veterinarian's assessment of the presence or strength of halitosis at a veterinary clinic. Furthermore, the information about halitosis may be information obtained at multiple different dates and times within a specific period, or information obtained by integrating such information. For example, information about halitosis may be obtained two, three, four, or ten times at different dates and times over a period of two weeks, one month, or three months, and then integrated by averaging or the like. By doing so, the information about halitosis is limited to information obtained at a specific date and time, which makes it possible to avoid using cases in which a person who normally does not have halitosis happens to have bad breath on that date and time for prediction.

[0017] The prediction unit of the present invention preferably uses information on the animal's intestinal bacteria in addition to information on the animal's bad breath for disease prediction. By using information on bad breath and information on intestinal bacteria, it is expected that disease prediction will be more accurate than when using only one of them.

[0018] The information about the intestinal bacteria of an animal may be any information about intestinal bacteria, for example, information or data about the number of intestinal bacterial species, the presence or absence, ratio, content rate, and diversity of specific bacteria or bacteria belonging to a specific family or phylum, and data about the diversity of the intestinal flora (also called diversity data) is preferred. Furthermore, it is preferable that the information about bad breath and the information about intestinal bacteria are obtained not too far apart, preferably not more than one year apart, more preferably not more than 180 days apart, and even more preferably not more than 60 days apart. This is because the information about bad breath and the information about intestinal bacteria are expected to more accurately reflect the condition of the animal if they are obtained closer together.

[0019] [Diversity Data] Diversity data is data related to the diversity of bacteria in an animal's intestinal microbiota. A high level of diversity in an intestinal microbiota means that the intestinal microbiota contains a wide range of different types of bacteria. There are several types of indicators representing diversity, so-called diversity indices, and any known indices may be used in the present invention. Examples of diversity indices include the Shannon-Wiener diversity index, also known as the Shannon index, and the Simpson diversity index, with the Shannon index being preferred.

[0020] [Measurement of Data Related to Intestinal Bacteria] Data related to intestinal bacteria, such as intestinal microbiota diversity data, can be measured using known metagenomic analysis methods such as amplicon sequencing using a sequencer such as NGS, or bacterial microbiota analysis methods. For example, a method is used in which a sample such as feces is collected from an animal and the DNA and RNA base sequence information of all organisms contained in the sample is analyzed using a next-generation sequencer to identify the organisms contained in the sample. A preferred method is to amplify all or part of the 16S rRNA gene contained in the sample as necessary, sequence it, and analyze the obtained sequence using software to obtain bacterial composition data in the sample. The bacterial composition data in the sample can be processed using software or by referring to gene databases such as Genbank, Greengenes, and SILVA databases to determine the species assignment of the bacteria contained in the sample, and then data on the occupancy and diversity of the animal's intestinal microbiota can be measured.

[0021] An example of amplicon analysis (bacterial flora analysis) of the 16S rRNA gene using next-generation sequencer (NGS) is described below. First, DNA is extracted from a sample such as feces using a DNA extraction reagent, and the 16S rRNA gene is amplified from the extracted DNA by PCR. The amplified DNA fragments are then comprehensively sequenced using NGS. Low-quality reads and chimeric sequences are then removed, and the sequences are clustered to perform operational taxonomic unit (OTU) analysis. An OTU is an operational taxonomic unit that treats sequences with a certain level of similarity (e.g., 96-97% or higher homology) as a single bacterial species. Therefore, the number of OTUs represents the number of bacterial species that make up the bacterial flora, and the number of reads belonging to the same OTU is thought to represent the relative abundance of that species. Furthermore, representative sequences can be selected from the number of reads belonging to each OTU, and the family name, genus, and species name can be identified through a database search. In this way, data on gut bacteria, such as the occupancy rate of bacteria belonging to a particular family or the diversity index of the gut microbiota, can be measured.

[0022] [Diseases] The types of diseases that can be treated in the present invention are not particularly limited, and include, for example, skin diseases, otic diseases, musculoskeletal diseases, ophthalmological diseases, digestive diseases, systemic diseases, urinary diseases, hepatic, biliary, and pancreatic diseases, circulatory diseases, nervous system diseases, respiratory diseases, dental and oral diseases, endocrine diseases, reproductive diseases, and blood and hematopoietic diseases. Skin diseases include, for example, dermatitis, atopic dermatitis, pyoderma, pathologically undetermined skin tumors, lipoma, histiocytoma (skin), mast cell tumor (skin), melanoma / melanoma, cutaneous lymphoma, perianal tumor (including perianal adenocarcinoma), other skin tumors, and allergic dermatitis. Otic diseases include, for example, otitis externa and otitis media. Musculoskeletal diseases include, for example, patellar dislocation and intervertebral disc herniation. Examples of ophthalmological diseases include conjunctivitis, eye discharge, keratitis, corneal ulcers / erosions, epiphora, cataracts, glaucoma, and uveitis. Examples of digestive system diseases include gastritis, enteritis, gastroenteritis, and inflammatory bowel disease (IBD). Examples of systemic diseases include loss of energy and collapse. Examples of urinary system diseases include cystitis, urolithiasis, and chronic kidney disease. Examples of hepatobiliary and pancreatic diseases include biliary sludge and chronic renal failure. Examples of circulatory system diseases include valvular disease and cardiomyopathy. Examples of nervous system diseases include epilepsy, seizures, and brain tumors. Examples of respiratory system diseases include coughing, rhinitis, tracheal collapse, and bronchial stenosis. Examples of dental and oral system diseases include periodontal disease, stomatitis, and oral tumors. Examples of endocrine system diseases include hypothyroidism and diabetes. Reproductive system diseases include, for example, mammary tumors and balanitis, and blood and hematopoietic system diseases include, for example, lymphoid tissue tumors, thrombocytopenia, multicentric lymphoma, angiosarcoma, and other lymphoid / hematopoietic tissue tumors.

[0023] The prediction unit of the present invention may be configured to make a prediction using a trained model. Such a trained model is preferably a trained model that has learned the relationship between information about an animal's halitosis, or information about the animal's halitosis and intestinal bacteria, and the information about the halitosis, information about whether the animal has suffered from a specific disease within a predetermined period since the acquisition of a sample for information analysis on intestinal bacteria or the analysis of its intestinal flora, or the information about the halitosis, or information about whether the animal had a disease at the time of acquisition of a sample for information analysis on intestinal bacteria or the analysis of its intestinal flora. The trained model is preferably a trained model that has been trained using as training data the information about the animal's halitosis, or information about the animal's halitosis and intestinal bacteria, and the information about the halitosis, information about whether the animal has suffered from a specific disease within a predetermined period since the acquisition of a sample for information analysis on intestinal bacteria or the analysis of its intestinal flora, or the information about the halitosis, or information about whether the animal had a disease at the time of acquisition of a sample for information analysis on intestinal bacteria or the analysis of its intestinal flora. The predetermined period for the information regarding whether or not an animal has contracted a specific disease within such a predetermined period used in the training data is preferably within three years, more preferably within two years, even more preferably within one year, and particularly preferably within 180 days. In the training data, whether or not an animal has contracted a disease can be replaced with a dummy variable. Information regarding whether or not an animal has contracted a disease within a predetermined period can be obtained from a veterinary clinic or an insured owner, for example, in connection with an insurance claim (also called an "accident").

[0024] The trained model is preferably artificial intelligence (AI). Artificial intelligence (AI) refers to software or systems that mimic the intellectual tasks performed by the human brain using a computer, specifically computer programs that understand natural language used by humans, perform logical inference, and learn from experience. AI may be either general-purpose or specialized, and may be a deep neural network, a convolutional neural network, or the like, and publicly available software can be used.

[0025] To generate a trained model, artificial intelligence is trained using training data. Either machine learning or deep learning can be used for the training, but machine learning is preferred. Deep learning is an advanced version of machine learning, and is characterized by its ability to automatically find features.

[0026] The learning method for generating a trained model is not particularly limited, and publicly available software can be used. For example, DIGITS (the Deep Learning GPU Training System) published by NVIDIA can be used. Alternatively, the trained model may be trained using a known support vector machine method, such as that published in "Introduction to Support Vector Machines" (Kyoritsu Shuppan).

[0027] Machine learning can be either unsupervised learning or supervised learning, but supervised learning is preferred. The supervised learning method is not particularly limited, and examples thereof include decision trees, ensemble learning, and gradient boosting. Examples of publicly available machine learning algorithms include XGBoost, CatBoost, and LightGBM.

[0028] A trained model may be generated for each individual disease, or one that can handle multiple diseases may be generated. When generating a trained model for each individual disease, training data is used to train an animal suffering from a specific disease, including information about halitosis, or information about halitosis and intestinal bacteria, acquired a predetermined period before the animal was infected with the disease, and information about the animal's halitosis, or information about halitosis and intestinal bacteria, acquired for comparison, about an animal that has not suffered from the disease for a predetermined period since the information about halitosis or the information about intestinal bacteria was acquired, and information about the animal that has not suffered from the disease for a predetermined period. When training multiple diseases together, for example, training can be performed in the same manner as described above without separating the training data by disease type.

[0029] [Acquisition Unit] The prediction system of the present invention preferably includes an acquisition unit. The acquisition unit accepts input of information regarding the bad breath of an animal whose disease onset is to be predicted, and information regarding intestinal bacteria. The configuration of the acquisition unit is not particularly limited, and any known configuration for accepting or acquiring information or data can be employed. The method for accepting information or data may be any method, such as inputting or transmitting data to a terminal. For example, the acquisition unit may be configured to accept transmission or input of diversity data from an external terminal or computer via a network such as the Internet.

[0030] [Output] The format of the output of the prediction results by the prediction unit of the present invention is not particularly limited. For example, a prediction determination can be output by displaying, for example, on the screen of a personal computer or mobile device connected to the system of the present invention via the Internet, such as "There is a possibility that the subject will develop a disease within the next year," "The possibility of developing a disease within the next year is low," or "There is a high possibility that the subject is currently developing a disease." The system can also display the probability as a numerical value, such as "The possibility of developing a digestive system disease within the next six months is 60%." It can also display a classification result according to a predefined classification, such as "Disease Risk: AA." It can also display a numerical value comparing the likelihood of developing a disease between a group with bad breath and a group without bad breath, such as "The likelihood of developing heart disease is twice as high as that of dogs without bad breath." Furthermore, if the result indicates a high likelihood of developing a disease, the system may also be configured to suggest a visit to a hospital or a health check. The disease prediction system of the present invention may also have a separate output unit that receives the prediction results from the prediction unit and outputs the determination results.

[0031] The disease prediction system of the present invention may further include a suggestion unit that suggests measures to reduce the likelihood of contracting a disease according to the disease prediction results. For example, the suggestion unit receives the prediction results output from the prediction unit and, according to the prediction results, can suggest or recommend a diet for preventing the disease for each predicted disease, a supplement containing bacteria that makes the disease less likely to occur, a low-salt, low-calorie diet, a low-carbohydrate diet, a diet menu, etc. The suggestion unit may have a trained model.

[0032] Furthermore, beverages, meals, and supplements for disease prevention can be manufactured or customized based on the prediction results output by the disease prediction system or disease prediction method of the present invention. Services related to disease prevention can also take the form of predictions using the disease prediction system or disease prediction method of the present invention, providing the prediction results, manufacturing or customizing beverages, meals, and supplements based on the prediction results, and proposing and recommending the beverages, meals, and supplements. Furthermore, after providing such services, it is also possible to further implement the disease prediction method of the present invention and indicate whether the tendency to develop a disease has improved. The beverages, meals, and supplements mentioned above include dietary beverages, diet foods, nutritional supplement additives, etc. In this way, proposing, manufacturing, and customizing meals and foods based on the prediction results is expected to delay the onset of disease and improve or alleviate symptoms.

[0033] [Insurance Premium Calculation System] The disease prediction system of the present invention can also be an insurance premium calculation system that includes an insurance premium calculation unit in addition to a prediction unit. The insurance premium calculation unit determines the insurance premium for the animal based on the disease incidence prediction predicted and output by the prediction unit using information about the halitosis of the animal that is the subject of insurance, or corrects or adjusts the insurance premium calculated based on basic information such as the animal's age, breed, and medical history in accordance with the disease incidence prediction result. In addition to the disease incidence prediction, information such as the animal's facial image, type, breed, age, sex, weight, and medical history may also be used to determine the insurance premium.

[0034] With the insurance premium calculation system of the present invention, in addition to applying for pet insurance, users can send information about their pet's bad breath and samples such as fecal samples, and a card (pet health insurance card) will be created indicating that they have pet insurance, and they can also obtain their pet's insurance premium and predictions of future disease incidence.

[0035] (Configuration Example) Hereinafter, an example of the configuration of the prediction system of the present invention will be described with reference to FIGS.

[0036] 1 and 19, terminal 40 is a terminal used by a user (owner, veterinarian, etc.). Examples of terminal 40 include a personal computer, smartphone, and tablet terminal. Terminal 40 includes a processing unit such as a CPU, a storage unit such as a hard disk, ROM, or RAM, a display unit such as an LCD panel, an input unit such as a mouse, keyboard, or touch panel, and a communication unit such as a network adapter. A user accesses server 1 from terminal 40 and inputs and transmits information about the animal's bad breath to be predicted, a facial image (photograph), and information about the animal's type, breed, age at the time of photographing, weight, medical history, and the like. Furthermore, by accessing server 1 with terminal 40, the user can receive disease prediction results from server 1.

[0037] Furthermore, when information about bad breath and information about intestinal bacteria are used for prediction by the prediction system, the user receives a fecal sample collection kit for examining the intestinal microbiota of the animal to be predicted and sends the fecal sample to a company that measures the intestinal microbiota (not shown). The company measures the intestinal microbiota of the animal and obtains data about the intestinal microbiota, such as the occupancy or ratio of specific bacteria or data about diversity such as the Shannon index. The company may then directly input and transmit the data about the intestinal microbiota of the pet to the server's acquisition unit 31 via its own terminal, or the company may separately send the data about the intestinal microbiota of the pet to the user by mail, email, etc., and the user may input and transmit the data about the intestinal microbiota to the acquisition unit 31 via the terminal 40. The prediction system of the present invention receives input, transmission, upload, etc. from the company or user and acquires information about intestinal bacteria using the acquisition unit 31. The method of analyzing the intestinal flora and the method of generating information about intestinal bacteria are not particularly limited. For example, instead of requesting a company to do the analysis, the user may analyze a fecal sample themselves to obtain information about intestinal bacteria.

[0038] In this embodiment, the server is configured by a computer, but any device may be used as long as it has the functions of the present invention. The storage unit 10 is configured, for example, by a ROM, a RAM, or a hard disk. The storage unit 10 stores information processing programs for operating each unit of the server, and in particular, a prediction program 11 and, if necessary, an insurance premium calculation program 12. If the system is configured as a disease prediction system that simply outputs a prediction of disease incidence without the purpose of calculating insurance premiums, the insurance premium calculation program 12 may be omitted.

[0039] The prediction program 11 is software or a program stored in a storage unit. When loaded into the processing and calculation unit (CPU) 20, the processing and calculation unit (CPU) 20 is configured as a prediction unit. As described above, the prediction program inputs information about the halitosis of the insured animal entered by the user or the company that performed the intestinal flora measurement, and outputs a prediction of whether the animal will develop a disease within a predetermined period (e.g., within six months) from the time the information about the halitosis was acquired or entered, or whether the animal currently has a disease. The prediction program is a program for predicting whether the animal will develop a disease within a predetermined period or whether the animal currently has a disease, based on information about the animal's halitosis. For example, if information indicating the presence of halitosis is input, the program will evaluate the likelihood of the animal developing a disease as high, and if information indicating the absence of halitosis is input, the program will evaluate the likelihood of the animal developing a disease as low. The program may also be a program for implementing an algorithm that calculates a score based on information about the presence or severity of halitosis according to preset criteria, and determines the risk of developing a disease based on a total score obtained by adding up the scores. That is, if the total score is equal to or greater than a predetermined value, the risk of developing a disease is high, and if the total score is less than the predetermined value, the risk of developing a disease is low. When calculating the score, in addition to information and data related to halitosis, basic information about the animal, such as age, breed, sex, medical history, and weight, can also be used. The prediction program may also include a trained model. The trained model is preferably a trained model that has learned the relationship between information about the animal's halitosis, or information about the animal's halitosis and information about intestinal bacteria, and information about whether the animal has suffered from a specific disease within a predetermined period from the time the information about the halitosis or the sample for analyzing information about intestinal bacteria was obtained or the intestinal flora was analyzed, or information about the halitosis or information about whether the animal had a disease at the time the sample for analyzing information about intestinal bacteria was obtained or the intestinal flora was analyzed.The trained model is preferably a trained model that has been trained using training data including information on the animal's halitosis, or information on the animal's halitosis and intestinal bacteria, and information on whether the animal has suffered from a specific disease within a predetermined period from the time the halitosis information, the time the sample for intestinal bacteria analysis was obtained, or the time the intestinal bacterial flora was analyzed, or information on whether the animal had a disease at the time the halitosis information, the time the sample for intestinal bacteria analysis was obtained, or the time the intestinal bacterial flora analysis was analyzed. The predetermined period for the information on whether the animal has suffered from a specific disease within a predetermined period used as training data is preferably within three years, more preferably within two years, even more preferably within one year, and particularly preferably within 180 days. The training data for whether the animal has suffered from a disease can be replaced with a dummy variable. Information on whether the animal has suffered from a disease within a predetermined period can be obtained, for example, from a veterinary clinic or an insured pet owner in connection with an insurance claim.

[0040] The insurance premium calculation program 12 is software or a program stored in a memory unit. When loaded into the processing and calculation unit (CPU) 20, the processing and calculation unit (CPU) 20 is configured as an insurance premium calculation unit, which calculates the insurance premium for the animal based on the disease incidence prediction output by the prediction unit and information input by the user, such as the animal's type, breed, age, weight, and medical history. For example, the software classifies insurance premiums based on the animal's type, breed, age, weight, medical history, etc., and finally modifies the grade taking into account the disease incidence prediction output by the prediction unit to calculate the final insurance premium. The insurance premium calculation program 12 may also include a trained model. An example of a trained model is a model that has learned the relationship between information about the animal's bad breath and the insurance premium set for the animal. Preferably, the trained model is a model that has trained using information about the animal's bad breath and the insurance premium set for the animal as training data. The trained model may be one that has been trained using information on intestinal bacteria, the type, breed, age, weight, medical history, and other information in addition to information on bad breath.

[0041] The processing operation unit 20 is, for example, a central processing unit (CPU), and predicts the onset of disease and calculates insurance premiums using a prediction program 11 and an insurance premium calculation program 12 stored in the storage unit.

[0042] The interface unit (communication unit) 30 includes an acquisition unit 31 and an output unit 32, and receives information about animal bad breath, intestinal flora diversity data, and other information from the user's terminal, and outputs predictions of disease incidence and insurance premium calculation results to the user's terminal.

[0043] (Example of embodiment) An example of an embodiment of the disease prediction system of the present invention will be described.

[0044] (1) Case where an Animal Patient Visits a Veterinary Hospital As an example of an embodiment of the disease prediction system of the present invention, a case where an animal patient (dog) visits a veterinary hospital will be described.

[0045] First, dog identification information (information for identifying the individual dog, such as the microchip identification number, a uniquely assigned ID, the owner's name, the dog's name, etc.) is obtained from the owner of a dog that visits the veterinary clinic through an interview or the like, and a questionnaire regarding the presence or absence of bad breath is also conducted. For example, if the result of the questionnaire is "bad breath," the identification information and the questionnaire result "bad breath" are entered into a terminal connected to the Internet, and the questionnaire result is uploaded via the terminal to the acquisition unit of the disease prediction system of the present invention.

[0046] Next, the acquisition unit acquires information regarding the diversity of the intestinal microbiota (such as the Shannon index) from a database based on the identification information of the animal, etc. In this case, the information regarding the diversity of the intestinal microbiota is stored in advance in the database. That is, a veterinarian receives the dog's feces from the owner and sends it to an intestinal bacteria analysis service provider. The analysis results received from the analysis service provider are then entered into the disease prediction terminal of the present invention, and the questionnaire results are uploaded to the acquisition unit of the disease prediction system of the present invention via the terminal. The uploaded information regarding the diversity of the intestinal microbiota is linked to the dog's identification information and stored in the database.

[0047] Finally, when the veterinarian sends a command to make a disease prediction to the disease prediction system of the present invention via the terminal, the prediction unit (the processing and calculation unit that reads the prediction program) predicts whether the target dog will develop a disease within a specified period based on the information about diversity and the presence or absence of bad breath, outputs the prediction to the output unit, and the output result is displayed on the terminal.

[0048] Based on this prediction, veterinarians can decide whether to perform tests to diagnose diseases, such as blood tests or ultrasound scans, and can also use this information to decide whether to recommend health checkups to pet owners.

[0049] (2) When an owner manages health using a terminal As an example of an embodiment of the disease prediction system of the present invention, we will explain a case where an owner accesses the disease prediction system of the present invention via a smartphone (mobile terminal) to manage the health of their pet dog.

[0050] Owners input information about the presence or absence of bad breath and the food currently being fed to their dog into the input screen of an app displayed on their smartphone. The input information from the app is then uploaded to the acquisition unit of the disease prediction system of the present invention via the Internet. Here, the information about food may include, for example, the number of different types of food. Research by the present inventors has shown that when comparing dogs that ate only one type of food with dogs that ate two or more types of food in combination, the dogs that ate two or more types of food in combination had a higher diversity index (Shannon index) ( Figure 2 ). Figure 2A is a graph showing the number of different types of food fed and the average Shannon index of the intestinal microbiota for 38,267 dogs (any breed, 0-3 years old). Figure 2B is a graph showing the number of different types of food fed and the average Shannon index of the intestinal microbiota for 39,672 cats (any breed, 0-3 years old). From each graph, it can be seen that dogs that ate a larger variety of food tend to have a higher Shannon index. Therefore, if the owner is only feeding one type of food to their pet, the suggestion unit will suggest feeding multiple types of food. In addition, since increasing the number of types of food is expected to increase the Shannon index and reduce the incidence rate, this prediction is sent to the owner's mobile device.

[0051] The disease prediction system of the present invention may also include a suggestion unit. The suggestion unit may, for example, select an oral care product based on information about bad breath, output the product, and transmit it to the owner's mobile device. This can be achieved by registering oral care products according to the level of bad breath and other information (age, breed, personality, etc.) in a database in advance, and when the acquisition unit receives information about the dog's bad breath and other information, the suggestion unit selects the product best suited to the dog from the database.

[0052] By adopting such an embodiment of the present invention, owners can learn not only the likelihood of their dog contracting a disease in the following year, but also measures to reduce the likelihood of their dog contracting the disease.

[0053] [Prediction method] The disease prediction method of the present invention is a method for predicting disease in animals, in which a computer uses information about the animal's halitosis to predict whether the animal will develop a disease within a predetermined period of time. The components of the information about the animal's halitosis, etc. are the same as those in the disease prediction system described above.

[0054] 3 is a diagram illustrating an overview of a disease prediction method according to one embodiment of the present invention. As shown in FIG. 3, the disease prediction method according to this embodiment predicts disease using information about an animal's halitosis and information about intestinal bacteria. Specifically, the method includes, in order, step S1 of acquiring information about the animal's halitosis, step S2 of acquiring information about intestinal bacteria, step S3 of predicting the animal's disease susceptibility based on the information about the animal's halitosis and the information about intestinal bacteria, and step S4 of outputting and transmitting the calculated prediction result of the disease susceptibility.

[0055] 20 is a diagram outlining a disease prediction method according to another embodiment of the present invention. As shown in FIG. 20, the disease prediction method according to this embodiment is a method for predicting disease using information about an animal's halitosis. That is, the method includes, in order, step S11 of acquiring information about the animal's halitosis, step S12 of predicting the possibility of the animal suffering from a disease based on the information about the animal's halitosis, and step S13 of outputting and transmitting the calculated prediction result of the possibility of the disease suffering.

[0056] [Health Condition Prediction System] The health condition prediction system of the present invention comprises an acquisition unit that acquires halitosis information related to the animal's halitosis, and a health condition prediction unit that uses the information about the halitosis to predict the health condition or future health condition of the animal. In the health condition prediction system of the present invention, it is preferable that the health condition prediction unit predicts the health condition or future health condition of the animal using information about the animal's halitosis and information about the animal's intestinal bacteria. Furthermore, in the health condition prediction system of the present invention, it is preferable that the information about the animal's intestinal bacteria is information about the diversity of the intestinal flora.

[0057] [Health Condition Prediction Unit] The health condition prediction unit of the present invention is a means for predicting and determining whether an animal's health condition will deteriorate within a predetermined period of time or whether its health condition has deteriorated based on information about the animal's bad breath. The health condition prediction unit is composed of, for example, a processing device such as a CPU, and calculates the possibility of a deterioration in the animal's health condition using a program for predicting and determining a health condition, a trained model, software including the trained model, or the program. The program, trained model, or software including the trained model may be stored in a separate storage device. The prediction and determination method used by the health condition prediction unit is not particularly limited. For example, a processor uses a preset program to predict and determine whether an animal's health condition will deteriorate within a predetermined period of time or whether its health condition is currently deteriorating based on information about the animal's bad breath. Alternatively, the configuration may include calculating a score based on information about the presence or severity of bad breath according to preset criteria, and determining the risk of a deterioration in the health condition based on a total score obtained by adding up the scores. In other words, if the total score is equal to or greater than a predetermined value, the risk of a deterioration in the health condition is high, and if the total score is less than the predetermined value, the risk of a deterioration in the health condition is low. When calculating the score, in addition to information and data related to bad breath, basic information about the animal, such as age, breed, sex, medical history, weight, etc., can also be used.

[0058] The health condition prediction unit of the present invention uses information about the animal's halitosis to predict whether the animal's health condition will deteriorate, whether a latent deterioration in the animal's health condition will become apparent, or whether the animal's health condition is currently deteriorating, preferably within a predetermined period, more preferably from the time of reception, the time of halitosis collection, or the time of determining the presence or severity of halitosis. The predetermined period is preferably within three years, more preferably within two years, even more preferably within one year, and particularly preferably within 180 days.

[0059] The information regarding bad breath is the same as above.

[0060] The health condition prediction unit of the present invention preferably uses information on the animal's intestinal bacteria in addition to information on the animal's bad breath to predict the health condition. By using information on bad breath and information on intestinal bacteria, it is expected that disease prediction will be more accurate than when using only one of them.

[0061] The information on animal intestinal bacteria is the same as above.

[0062] The health condition of the animal in this invention refers to, for example, whether the animal is suffering from any disease, whether the animal's mental state is good, or whether the animal's coat is in good condition. Specifically, whether the animal's mental state is good refers to, for example, whether the animal dislikes strangers, whether the animal dislikes strange animals, or whether the animal is aggressive.

[0063] [Health condition prediction program] The health condition prediction program of the present invention causes a computer to execute the steps of acquiring halitosis information related to an animal's halitosis, and predicting the animal's health condition or future health condition using the information related to the halitosis.

[0064] The health condition prediction program is a program for predicting whether an animal's health condition will deteriorate within a predetermined period of time or whether its health condition is currently deteriorating, based on information about the animal's halitosis. For example, the program is a program for executing a method that inputs information about the halitosis of a target animal entered by a user or a company that performed an intestinal flora measurement, and outputs a prediction of whether the animal's health condition will deteriorate within a predetermined period of time (e.g., within six months) from the time the information about the halitosis was acquired or entered, or whether its health condition is currently deteriorating. The health condition prediction program is a program for implementing an algorithm that calculates a score based on information about the presence or severity of halitosis according to preset criteria and determines the risk of a health condition deterioration based on a total score obtained by adding up the scores. That is, if the total score is equal to or greater than a predetermined value, the risk of a health condition deterioration is high, and if the total score is less than the predetermined value, the risk of a health condition deterioration is low. When calculating the score, basic information about the animal, such as age, breed, sex, medical history, and weight, can be used in addition to information and data about halitosis. The health condition prediction program may also include a trained model. The trained model is preferably a trained model that has learned the relationship between information about an animal's halitosis, or information about an animal's halitosis and information about intestinal bacteria, and the information about the halitosis, information about whether the animal's health condition has deteriorated within a predetermined period since the information about the halitosis, or the time when the sample for information analysis on intestinal bacteria was obtained or the intestinal flora was analyzed, or information about the halitosis, or information about whether the health condition had deteriorated at the time when the sample for information analysis on intestinal bacteria was obtained or the intestinal flora was analyzed.The trained model is preferably a trained model that has been trained using as training data information about the animal's halitosis, or information about the animal's halitosis and information about intestinal bacteria, and the information about whether the animal's health condition had deteriorated within a predetermined period since the information about the halitosis, or the time when the sample for information analysis on intestinal bacteria was obtained or the intestinal flora was analyzed, or information about whether the health condition had deteriorated at the time when the sample for information analysis on intestinal bacteria was obtained or the intestinal flora was analyzed.The predetermined period for the information regarding whether or not the health condition of an animal has deteriorated within such a predetermined period used in the training data is preferably within three years, more preferably within two years, even more preferably within one year, and particularly preferably within 180 days. In the training data, whether or not the health condition has deteriorated can be replaced with a dummy variable. Information regarding whether or not an animal's health condition has deteriorated within a predetermined period can be obtained, for example, from a veterinary clinic or an insured owner in connection with an insurance claim.

[0065] The medium on which the health condition prediction program is recorded is not particularly limited, and may be stored, for example, on a disc such as a DVD or Blu-ray, or on a memory card, or on an HDD, SSD, magnetic tape for recording data, or other storage medium.

[0066] [Information Processing System] The information processing system of the present invention includes an acquisition unit that acquires information regarding an animal's bad breath, and an output unit that outputs information encouraging care of the animal if the animal has bad breath.

[0067] The acquisition unit is the same as above.

[0068] [Output Unit] The output unit is, for example, a processor such as a CPU, and uses an output program or software to output information based on the prediction result. The program or software may be stored in a separate storage device. The format of the output information is not particularly limited. The processing by the suggestion unit described above is an example of information based on the prediction result, which encourages the owner to take care of the animal. For example, a computer may issue an instruction to an owner's electronic device, such as a smartphone or tablet, to display a notice encouraging the owner to take care of the animal. In addition, in an embodiment in which the method of the present invention is executed by a terminal, a message or icon encouraging the owner to take care of the animal may be displayed on an app within the terminal. Specific examples of the display of information encouraging the owner to take care of the animal include messages such as "Your dog's bad breath has been detected. For your dog's health, please take care of your periodontal disease," or "Your cat has bad breath. For your cat's health, please take care of your intestinal health," as well as the display of an icon or mark to alert the owner. The care is preferably oral care, and examples of oral care include periodontal care, tartar removal, and oral disinfection.

[0069] [Program] The information processing program of the present invention causes a computer to execute the steps of acquiring information about an animal's bad breath, and, if the animal has bad breath, encouraging care for the animal based on the information about the bad breath.

[0070] The following are examples of the present invention, but the present invention is not limited to these examples.

[0071] As described below, by dividing animals into groups with and without halitosis and comparing the incidence of diseases, it was found that there were differences in the incidence or prevalence of each disease. Based on these results, the prediction unit may predict whether an animal with halitosis will develop a disease within a specified period of time or whether it currently has a disease. The prediction unit may also make a prediction based on the difference in incidence between the group with halitosis and the group without halitosis. For example, the difference in incidence or prevalence for each disease between the group with halitosis and the group without halitosis may be stored in a database, and the prediction unit may access the database and use information regarding the difference in incidence or prevalence to predict disease development. Based on these results, the output unit may also output the difference in incidence for each disease.

[0072] Reference Example 1 (Information on Halitosis and Disease Prevalence) A questionnaire was administered to the owners of 97,902 dogs, regardless of breed or age, regarding the presence or absence of halitosis, and information on halitosis was obtained. The population was then divided into two groups based on the survey results: 21,355 dogs with halitosis and 76,547 dogs without halitosis. Insurance claim records for each group were tracked for one year from the time the survey was taken. As a result, it was possible to determine whether insurance claims had been made for each dog in each group from the time the survey was taken until the following year, i.e., whether the dog had developed a disease. The prevalence of each disease is shown in the graphs of Figures 4 to 9. Note that injuries such as fractures were excluded from the insurance claim records (same below).

[0073] Figure 4 is a graph showing the incidence rate of heart disease (circulatory disease). Looking at this, 0.36% of individuals in the group without bad breath developed heart disease within one year after the questionnaire was taken. In contrast, 0.65% of individuals in the group with bad breath developed heart disease within one year after the questionnaire was taken. Thus, the group with bad breath had a higher incidence of heart disease.

[0074] Figure 5 is a graph showing the incidence rate of kidney disease. Looking at this, 0.17% of individuals in the group without bad breath developed kidney disease within one year after the questionnaire was taken. In contrast, 0.42% of individuals in the group with bad breath developed kidney disease within one year after the questionnaire was taken. Thus, the group with bad breath had a higher incidence of kidney disease.

[0075] Figure 6 is a graph showing the incidence rate of tumors. Looking at this, 0.76% of individuals in the group without bad breath developed tumors within one year after the questionnaire was taken. In contrast, 1.22% of individuals in the group with bad breath developed tumors within one year after the questionnaire was taken. Thus, the group with bad breath had a higher incidence of tumors.

[0076] Figure 7 is a graph showing the incidence of dental and oral diseases. As can be seen from this, in the group without bad breath, 1.51% of individuals suffered from dental and oral diseases within one year after the questionnaire was taken. In contrast, in the group with bad breath, 4.76% of individuals suffered from dental and oral diseases within one year after the questionnaire was taken. Thus, the group with bad breath had a higher incidence of dental and oral diseases.

[0077] Figure 8 is a graph showing the incidence rate of respiratory diseases. Looking at this, it can be seen that in the group without bad breath, 1.25% of individuals suffered from respiratory diseases within one year after the questionnaire was taken. In contrast, in the group with bad breath, 1.48% of individuals suffered from respiratory diseases within one year after the questionnaire was taken. Thus, the group with bad breath had a higher incidence of respiratory diseases.

[0078] Figure 9 is a graph showing the incidence rate of liver, gallbladder, and pancreatic diseases. Looking at this, it can be seen that in the group without bad breath, 1.15% of individuals developed liver, gallbladder, and pancreatic diseases within one year after the questionnaire was taken. In contrast, in the group with bad breath, 1.56% of individuals developed liver, gallbladder, and pancreatic diseases within one year after the questionnaire was taken. Thus, the group with bad breath had a higher incidence of liver, gallbladder, and pancreatic diseases.

[0079] [Reference Example 2] For 166,137 dogs aged 0 to 16 years, regardless of breed, owners were asked to complete a questionnaire regarding the presence or absence of halitosis, and information regarding halitosis was obtained. In addition, the Shannon index of the intestinal flora was measured from fecal samples of these 166,137 dogs according to the following procedure.

[0080] (DNA Extraction from Fecal Samples) Fecal samples were collected from each dog and DNA was extracted as follows. Dog owners collected fecal samples using a fecal collection kit. The fecal samples were received and suspended in water. Next, 200 μL of the fecal suspension and 810 μL of lysis buffer (containing 224 μg / mL of Proteinase K) were added to a bead tube, and the samples were bead-disrupted using a bead homogenizer (6,000 rpm, 20-second disruption, 30-second interval, 20-second disruption). The samples were then treated with Proteinase K by placing them on a 70°C heat block for 10 minutes, and then inactivated by placing them on a 95°C heat block for 5 minutes. The lysed specimen was subjected to automatic DNA extraction using Chemagic 360 (PerkinElmer) according to the Chemagic kit tool protocol, to obtain 100 μL of DNA extract.

[0081] (Meta-16S RNA gene sequence analysis) Meta-16S sequence analysis was performed using a modified version of Illumina 16S Metagenomic Sequencing Library Preparation (version 15044223 B). First, a 460 bp region containing the variable region V3-V4 of the 16S rRNA gene was amplified by PCR using universal primers (Illumina_16S_341F and Illumina_16S_805RPCR). The PCR reaction mixture was prepared by mixing 10 uL of DNA extract, 0.05 uL of each primer (100 uM), 12.5 uL of 2xKAPA HiFi Hot-Start ReadyMix (F. Hoffmann-LaRoche, Switzerland), and 2.4 uL of PCR-grade water. After heat denaturation at 95 ° C for 3 minutes, PCR was performed by repeating 30 cycles of 95 ° C for 30 seconds, 55 ° C for 30 seconds, and 72 ° C for 30 seconds, followed by a final extension reaction at 72 ° C for 5 minutes. The amplified product was purified using magnetic beads and eluted with 50 uL of Buffer EB (QIAGEN, Germany). The purified amplified product was subjected to PCR using Nextera XT Index Kit v2 (Illumina, CA, US) and indexed. The PCR reaction mixture was prepared by mixing 2.5 uL of the amplified product, 2.5 uL of each primer, 12.5 uL of 2x KAPA HiFi Hot-Start ReadyMix, and 5 uL of PCR-grade water. The PCR involved heat denaturation at 95°C for 3 minutes, followed by 12 cycles of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds, followed by a final extension reaction at 72°C for 5 minutes. The indexed amplified product was purified using magnetic beads and eluted with 80-105 uL of Buffer EB. The concentration of each amplification product was measured using a NanoPhotometer (Implen, CA, US) and adjusted to 1.4 nM. Equal amounts were mixed to prepare the sequencing library. The DNA concentration and amplification product size of the sequencing library were confirmed by electrophoresis and analyzed using MiSeq. MiSeq Reagent Kit V3 was used for analysis, and 2 x 300 bp paired-end sequencing was performed. The resulting sequences were analyzed using MiSeq Reporter to obtain bacterial composition data.The sequences of the universal primers used above are as follows. These universal primers can be purchased commercially: Illumina_16S_341F 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3′ Illumina_16S_805R 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3′.

[0082] According to the above method, the composition data of the intestinal microbiota was obtained, and the Shannon index (Shannon-Wiener diversity index) was measured. The Shannon index (Shannon-Wiener diversity index) was calculated using QIIME2.

[0083] (Investigation of the Presence or Absence of Disease) All of the above dogs were investigated to see if they had contracted a disease within 180 days after the collection of a fecal sample. The presence or absence of a disease was investigated using pet insurance claim records to see if an insurance claim had been made within 180 days after the date of fecal collection for each individual whose feces had been collected. Note that the investigation was conducted on contracts in which no insurance claim had been made within 90 days prior to the date of fecal collection, regardless of whether the dog had contracted a disease. The absence of an insurance claim within 90 days prior to the date of fecal collection presumably meant that the dog had not contracted a disease before the fecal collection.

[0084] The 166,187 dogs were divided into three classes based on their Shannon Index values. That is, dogs with a Shannon Index of 0 to less than 3.6 were classified as a low diversity group, dogs with a Shannon Index of 3.6 to less than 4.3 were classified as a medium diversity group, and dogs with a Shannon Index of 4.3 to 7.0 were classified as a high diversity group. Each of the three groups was further divided into a group with and a group without halitosis. Figure 10 shows a graph of the incidence of kidney disease for each group.

[0085] FIG. 11 shows a graph of the incidence of kidney disease, similar to that described above, for only individuals aged 0 to 7 years selected from the population of 166,187 animals.

[0086] FIG. 12 shows a graph of the incidence of kidney disease, similar to that described above, for only individuals aged 8 to 16 years selected from the population of 166,187 animals.

[0087] As is clear from Figures 10 to 12, the higher the diversity of the intestinal microbiota, the lower the incidence of kidney disease. Furthermore, within each diversity group, those without bad breath have a lower incidence of kidney disease. In other words, it was found that by using information on bad breath and information on intestinal bacteria, it is possible to accurately predict the incidence of disease.

[0088] [Reference Example 3] For 102,853 cats aged 0 to 12 years, regardless of breed, owners were asked to complete a questionnaire regarding the presence or absence of halitosis, and information regarding halitosis was obtained. In addition, the Shannon index of the intestinal flora was measured from fecal samples of these 102,853 cats using the same procedure as above.

[0089] All of the cats were investigated to see if they had contracted a disease within 180 days after the fecal sample was collected. The presence or absence of a disease was investigated using pet insurance claim records to see if an insurance claim was made within 180 days after the date of fecal collection for each cat whose feces were collected. Note that the investigation was conducted on contracts in which no insurance claim was made within 90 days prior to the fecal collection date, regardless of whether the cat had a disease. The absence of an insurance claim within 90 days prior to the fecal collection date is presumed to mean that the cat was not contracting a disease before the fecal collection date.

[0090] The 102,853 cats were divided into three classes based on their Shannon index values. Specifically, cats with a Shannon index of 0 to less than 4.95 were classified as a low diversity group, cats with a Shannon index of 4.95 to less than 5.36 were classified as a medium diversity group, and cats with a Shannon index of 5.36 to 7.46 were classified as a high diversity group. Each of the three groups was further divided into groups with and without halitosis. Figure 13 shows a graph of the incidence of kidney disease for each group.

[0091] FIG. 14 shows a graph of the incidence of kidney disease, similar to that described above, for only individuals aged 0 to 6 years selected from the population of 102,853 animals.

[0092] FIG. 15 shows a graph of the incidence of kidney disease, similar to that described above, for only individuals aged 7 to 12 years selected from the population of 102,853 animals.

[0093] As is clear from Figures 13 to 15, the higher the diversity of the intestinal microbiota, the lower the incidence of kidney disease. Furthermore, within each diversity group, those without bad breath have a lower incidence of kidney disease. In other words, it was found that by using information on bad breath and information on intestinal bacteria, it is possible to accurately predict the incidence of disease.

[0094] (Reference Example 4) FIG. 16 shows a graph of the relationship between the presence or absence of bad breath, data on the diversity of intestinal bacterial flora, and the incidence rate of heart disease for 166,137 animals in the same population as in Reference Example 2.

[0095] FIG. 17 shows a graph of the incidence of heart disease, similar to the above, for only individuals aged 0 to 7 years selected from the population of 166,137 animals.

[0096] FIG. 18 shows a graph of the incidence of heart disease, similar to that described above, for only individuals aged 8 to 16 years selected from the population of 166,137 animals.

[0097] As is clear from Figures 16 to 18, the higher the diversity of the intestinal microbiota, the lower the incidence of heart disease. Furthermore, within each diversity group, individuals without bad breath have a lower incidence of heart disease. In other words, it was found that by using information on bad breath and information on intestinal bacteria, it is possible to accurately predict the incidence of disease.

[0098] (Reference Example 5) (Periodontal Disease) A questionnaire was given to the owners of 157,034 dogs enrolled in pet insurance regarding the presence or absence of halitosis, and information regarding halitosis was obtained and registered in the pet insurance database. The 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to periodontal disease was confirmed from the insurance claim records for each group, and the insurance claim rate was calculated. The insurance claim rate due to periodontal disease was used as the prevalence rate of periodontal disease, and the relationship between halitosis and the prevalence rate of periodontal disease was graphed. The results are shown in Figure 21.

[0099] From FIG. 21, it can be seen that the group with bad breath had a higher prevalence of periodontal disease.

[0100] (Reference Example 6) (Stomatitis) As in the above, 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to stomatitis was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to stomatitis was used as the prevalence rate of stomatitis, and the relationship between halitosis and the prevalence rate of stomatitis was graphed. The results are shown in Figure 22.

[0101] From FIG. 22, it can be seen that the prevalence of stomatitis was higher in the group with bad breath.

[0102] (Reference Example 7) (Oral Tumor) As described above, 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to oral tumors was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to oral tumors was used as the prevalence rate of oral tumors, and the relationship between halitosis and the prevalence rate of oral tumors was plotted in a graph. The results are shown in Figure 23.

[0103] From FIG. 23, it was found that the group with bad breath had a higher prevalence of oral tumors.

[0104] (Reference Example 8) (Digestive System Disease) As in the above, 157,034 dogs were divided into a group with bad breath and a group without bad breath, and the presence or absence of insurance claims due to digestive system diseases (inflammatory bowel disease (IBD), digestive system tumors other than gastrointestinal lymphoma, and abdominal pain / colic of undetermined cause) was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to digestive system diseases was used as the prevalence rate of digestive system diseases, and the relationship between bad breath and the prevalence rate of digestive system diseases was graphed. The results are shown in Figure 24.

[0105] From FIG. 24, it can be seen that the group with bad breath had a higher prevalence of digestive disorders.

[0106] (Reference Example 9) (Blood and Hematopoietic Diseases) As in the above, 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to blood and hematopoietic diseases (lymphoid tissue tumors, thrombocytopenia, multicentric lymphoma, hemangiosarcoma, and lymphoid / hematopoietic tissue tumors other than these) was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to blood and hematopoietic diseases was used as the prevalence rate of blood and hematopoietic diseases, and the relationship between halitosis and the prevalence rate of blood and hematopoietic diseases was graphed. The results are shown in Figure 25.

[0107] From FIG. 25, it can be seen that the group with bad breath had a higher prevalence of blood and hematopoietic diseases.

[0108] (Reference Example 10) (Tumor of blood and hematopoietic organs) As in the above, 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to tumors of the blood and hematopoietic organs (multicentric lymphoma, hemangiosarcoma, and tumors of lymphoid tissue / hematopoietic tissue other than these) was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to tumors of the blood and hematopoietic organs was used as the prevalence rate of tumors of blood and hematopoietic diseases, and the relationship between halitosis and the prevalence rate of tumors of the blood and hematopoietic organs was graphed. The results are shown in Figure 26.

[0109] From FIG. 26, it can be seen that the group with bad breath had a higher prevalence of tumors of the blood and hematopoietic system.

[0110] (Reference Example 11) (Nervous System Disease) As in the above, 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to nervous system diseases (epilepsy, seizures, and brain tumors) was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to nervous system diseases was used as the prevalence rate of nervous system diseases, and the relationship between halitosis and the prevalence rate of nervous system diseases was graphed. The results are shown in Figure 27.

[0111] Figure 27 shows that the group with bad breath had a higher prevalence of neurological disorders.

[0112] (Reference Example 12) (Epilepsy) As in the above, 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to epilepsy was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to epilepsy was used as the prevalence rate of epilepsy, and the relationship between halitosis and the prevalence rate of epilepsy was graphed. The results are shown in Figure 28.

[0113] From Figure 28, it can be seen that the group with bad breath had a higher prevalence of epilepsy.

[0114] (Reference Example 13) (Brain tumor) As in the above, 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to brain tumors was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to brain tumors was used as the prevalence rate of brain tumors, and the relationship between halitosis and the prevalence rate of brain tumors was graphed. The results are shown in Figure 29.

[0115] Figure 29 shows that the group with bad breath had a higher prevalence of brain tumors.

[0116] (Reference Example 14) (Endocrine System Disease) As in the above, 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to endocrine system diseases (hypothyroidism and diabetes) was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to endocrine system diseases was used as the prevalence rate of endocrine system diseases, and the relationship between halitosis and the prevalence rate of endocrine system diseases was graphed. The results are shown in Figure 30.

[0117] From FIG. 30, it can be seen that the group with bad breath had a higher prevalence of endocrine system diseases.

[0118] (Reference Example 15) (Diabetes) As in the above, 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to diabetes was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to diabetes was used as the prevalence rate of diabetes, and the relationship between halitosis and the prevalence rate of diabetes was graphed. The results are shown in Figure 31.

[0119] From FIG. 31, it can be seen that the group with bad breath had a higher prevalence of diabetes.

[0120] (Reference Example 16) (Uveitis) As in the above, 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to uveitis was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to uveitis was used as the prevalence rate of uveitis, and the relationship between halitosis and the prevalence rate of uveitis was plotted in a graph. The results are shown in Figure 32.

[0121] Figure 32 shows that the prevalence of uveitis was higher in the group with bad breath.

[0122] (Reference Example 17) (Loss of vitality) As in the above, 157,034 dogs were divided into a group with bad breath and a group without bad breath, and the presence or absence of insurance claims due to loss of vitality was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to loss of vitality was taken as the prevalence of loss of vitality, and the relationship between the prevalence of bad breath and loss of vitality was graphed. The results are shown in Figure 33.

[0123] From FIG. 33, it can be seen that the prevalence of loss of energy was higher in the group with bad breath.

[0124] (Reference Example 18) (Mortality Rate) As in the above, 157,034 dogs were divided into a group with bad breath and a group without bad breath, and the presence or absence of death within one year from the time of questionnaire collection was confirmed, and the mortality rate was calculated. The relationship between bad breath and mortality rate was graphed. The results are shown in Figure 34.

[0125] From FIG. 34, it can be seen that the group with bad breath had a higher mortality rate.

[0126] (Reference Example 19) (Coat gloss) As in the above, 157,034 dogs were divided into a group with bad breath and a group without bad breath, and the results of a separate questionnaire obtained from owners on whether or not they were concerned about the coat gloss of their dog were compared to calculate the proportion of people who were concerned about coat gloss.The relationship between bad breath and the proportion of people who were concerned about coat gloss was then graphed.The results are shown in Figure 35.

[0127] From Figure 35, it can be seen that the group with bad breath is more likely to have poor coat luster.

[0128] (Reference Example 20) (Shyness) As in the above, 157,034 dogs were divided into a group with bad breath and a group without bad breath, and the results of a separate questionnaire obtained from the owners on whether or not the dogs were shy (whether or not they dislike strangers) were compared to calculate the proportion of dogs that were shy.The relationship between bad breath and the proportion of dogs that were shy was then graphed.The results are shown in Figure 36.

[0129] From Figure 36, it can be seen that the group with bad breath is more likely to be shy around strangers.

[0130] (Reference Example 21) (Animal Shyness) As in the above, 157,034 dogs were divided into a group with bad breath and a group without bad breath, and the results of a separate questionnaire obtained from the owners on whether or not the dogs were animal shy (whether or not they were afraid of unknown animals) were compared to calculate the proportion of dogs that were animal shy.The relationship between bad breath and the proportion of dogs that were animal shy was then graphed.The results are shown in Figure 37.

[0131] From Figure 37, it can be seen that the group with bad breath is more likely to be afraid of animals.

[0132] (Reference Example 22) (Skin Tumors) As above, 157,034 dogs were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to skin tumors (skin tumors of undetermined pathology, lipoma, histiocytoma (skin), mast cell tumor (skin), melanocytoma / melanoma, cutaneous lymphoma, perianal tumor (including perianal adenocarcinoma), and skin tumors not falling under the above categories) was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to skin tumors was used as the prevalence of skin tumors, and the relationship between halitosis and the prevalence of skin tumors was graphed. The results are shown in Figure 38.

[0133] From Figure 38, it can be seen that the group with bad breath had a higher prevalence of skin tumors.

[0134] (Reference Example 23) (Periodontal Disease) A questionnaire was given to the owners of 59,627 cats enrolled in pet insurance regarding the presence or absence of halitosis, and information regarding halitosis was obtained and registered in the pet insurance database. The 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to periodontal disease was confirmed from the insurance claim records for each group, and the insurance claim rate was calculated. The insurance claim rate due to periodontal disease was used as the prevalence rate of periodontal disease, and the relationship between halitosis and the prevalence rate of periodontal disease was graphed. The results are shown in Figure 39.

[0135] From FIG. 39, it can be seen that the group with bad breath had a higher prevalence of periodontal disease.

[0136] (Reference Example 24) (Stomatitis) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to stomatitis was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to stomatitis was used as the prevalence rate of stomatitis, and the relationship between halitosis and the prevalence rate of stomatitis was graphed. The results are shown in Figure 40.

[0137] From Figure 40, it can be seen that the group with bad breath had a higher prevalence of stomatitis.

[0138] (Reference Example 25) (Oral Tumor) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to oral tumors was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to oral tumors was used as the prevalence rate of oral tumors, and the relationship between halitosis and the prevalence rate of oral tumors was plotted in a graph. The results are shown in Figure 41.

[0139] From Figure 41, it can be seen that the group with bad breath had a higher prevalence of oral tumors.

[0140] (Reference Example 26) (Digestive Disease) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to digestive diseases (inflammatory bowel disease (IBD), digestive tumors other than gastrointestinal lymphoma, and abdominal pain / colic of undetermined cause) was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to digestive diseases was used as the prevalence rate of digestive diseases, and the relationship between halitosis and the prevalence rate of digestive diseases was graphed. The results are shown in Figure 42.

[0141] From Figure 42, it can be seen that the group with bad breath had a higher prevalence of digestive disorders.

[0142] (Reference Example 27) (Gastritis, Gastroenteritis, Enteritis) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to gastritis, gastroenteritis, or enteritis was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to gastritis, gastroenteritis, or enteritis was used as the prevalence rate of gastritis, gastroenteritis, or enteritis, and the relationship between halitosis and the prevalence rate of gastritis, gastroenteritis, or enteritis was plotted in a graph. The results are shown in Figure 43.

[0143] From FIG. 43, it can be seen that the group with bad breath had a higher prevalence of gastritis, gastroenteritis, or enteritis.

[0144] (Reference Example 28) (Cardiovascular disease) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to cardiovascular disease (valvular disease and cardiomyopathy) was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to cardiovascular disease was used as the prevalence rate of cardiovascular disease, and the relationship between halitosis and the prevalence rate of cardiovascular disease was graphed. The results are shown in Figure 44.

[0145] From Figure 44, it can be seen that the group with bad breath had a higher prevalence of cardiovascular diseases.

[0146] (Reference Example 29) (Valvular disease) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to valvular disease was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to valvular disease was used as the prevalence rate of valvular disease, and the relationship between halitosis and the prevalence rate of valvular disease was graphed. The results are shown in Figure 45.

[0147] From FIG. 45, it can be seen that the group with bad breath had a higher prevalence of valvular disease.

[0148] (Reference Example 30) (Chronic kidney disease) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to chronic kidney disease was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to chronic kidney disease was used as the prevalence rate of chronic kidney disease, and the relationship between halitosis and the prevalence rate of chronic kidney disease was graphed. The results are shown in Figure 46.

[0149] Figure 46 shows that the group with bad breath had a higher prevalence of chronic kidney disease.

[0150] (Reference Example 31) (Atopic Dermatitis) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to atopic dermatitis was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to atopic dermatitis was used as the prevalence rate of atopic dermatitis, and the relationship between halitosis and the prevalence rate of atopic dermatitis was plotted in a graph. The results are shown in Figure 47.

[0151] From Figure 47, it can be seen that the group with bad breath had a higher prevalence of atopic dermatitis.

[0152] (Reference Example 32) (Allergic Dermatitis) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to allergic dermatitis was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to allergic dermatitis was taken as the prevalence rate of allergic dermatitis, and the relationship between halitosis and the prevalence rate of allergic dermatitis was plotted in a graph. The results are shown in Figure 48.

[0153] From Figure 48, it can be seen that the prevalence of allergic dermatitis was higher in the group with bad breath.

[0154] (Reference Example 33) (Diabetes) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to diabetes was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to diabetes was used as the prevalence rate of diabetes, and the relationship between halitosis and the prevalence rate of diabetes was graphed. The results are shown in Figure 49.

[0155] From Figure 49, it can be seen that the group with bad breath had a higher prevalence of diabetes.

[0156] (Reference Example 34) (Neoplastic Disease) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to neoplastic disease was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to neoplastic disease was taken as the prevalence rate of neoplastic disease, and the relationship between halitosis and the prevalence rate of neoplastic disease was graphed. The results are shown in Figure 50.

[0157] From Figure 50, it can be seen that the group with bad breath had a higher prevalence of neoplastic diseases.

[0158] (Reference Example 35) (Blood and immune system diseases) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to blood and immune system diseases (lymphoid tissue tumors, thrombocytopenia, multicentric lymphoma, hemangiosarcoma, and lymphoid tissue / hematopoietic tissue tumors other than these) was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to blood and immune system diseases was used as the prevalence rate of neoplastic diseases, and the relationship between halitosis and the prevalence rate of blood and immune system diseases was graphed. The results are shown in Figure 51.

[0159] Figure 51 shows that the group with bad breath had a higher prevalence of blood and immune system diseases.

[0160] (Reference Example 36) (Tumor of blood and hematopoietic organs) As in the above, 59,627 cats were divided into a group with halitosis and a group without halitosis, and the presence or absence of insurance claims due to tumors of the blood and hematopoietic organs was confirmed, and the insurance claim rate was calculated. The insurance claim rate due to tumors of the blood and hematopoietic organs was used as the prevalence rate of tumors of the blood and hematopoietic organs, and the relationship between halitosis and the prevalence rate of tumors of the blood and hematopoietic organs was graphed. The results are shown in Figure 52.

[0161] From FIG. 52, it was found that the group with bad breath had a higher prevalence of blood and hematopoietic tumors.

[0162] (Reference Example 37) (Mortality rate) As in the above, 59,627 cats were divided into a group with bad breath and a group without bad breath, and the presence or absence of death within one year from the time of questionnaire collection was confirmed, and the mortality rate was calculated. The relationship between bad breath and mortality rate was graphed. The results are shown in Figure 53.

[0163] From Figure 53, it can be seen that the group with bad breath had a higher mortality rate.

[0164] (Reference Example 38) (Coat gloss) As in the above, 59,627 cats were divided into a group with bad breath and a group without bad breath, and the results of a separate questionnaire obtained from the owners on whether or not they were bothered by their cat's coat gloss were compared to calculate the percentage of cats who were bothered by coat gloss.The relationship between bad breath and the percentage of cats who were bothered by coat gloss was then graphed.The results are shown in Figure 54.

[0165] From Figure 54, it can be seen that the group with bad breath is more likely to have poor coat luster.

[0166] (Reference Example 39) (Shyness) As in the above, 59,627 cats were divided into a group with bad breath and a group without bad breath, and the proportion of cats that were shy was calculated based on the results of a separate questionnaire obtained from the owners regarding whether or not the cats were shy (whether or not they dislike strangers).The relationship between bad breath and the proportion of cats that were shy was then graphed.The results are shown in Figure 55.

[0167] From Figure 55, it can be seen that the group with bad breath is more likely to be shy around strangers.

[0168] (Reference Example 40) (Animal Shyness) As in the above, 59,627 cats were divided into a group with bad breath and a group without bad breath, and the results of a separate questionnaire obtained from the owners on whether or not the cats were animal shy (whether or not they were afraid of unknown animals) were compared to calculate the proportion of cats that were animal shy.The relationship between bad breath and the proportion of cats that were animal shy was then graphed.The results are shown in Figure 56.

[0169] From Figure 56, it can be seen that the group with bad breath is more likely to be afraid of animals.

Claims

1. A disease prediction system comprising: a prediction unit that uses information about an animal's bad breath to predict whether the animal will develop a disease within a specified period of time or whether the animal is currently suffering from a disease.

2. The disease prediction system according to claim 1, further comprising a suggestion unit that suggests measures to reduce the possibility of contracting a disease.

3. A disease prediction system according to claim 1, wherein the information regarding the animal's bad breath is obtained from a questionnaire regarding the presence or absence of bad breath obtained from the animal's owner or manager, or from the results of odor measurement using an odor determination device.

4. A disease prediction system as described in claim 1, wherein the information regarding the animal's bad breath is information regarding the presence or absence of bad breath at different dates and times acquired during a specified period.

5. A disease prediction system as described in claim 1, wherein the prediction unit uses information regarding the animal's bad breath and information regarding the animal's intestinal bacteria to predict whether the animal will develop a disease within a specified period of time or whether the animal is currently suffering from a disease.

6. The disease prediction system according to claim 5, wherein the information regarding the intestinal bacteria of the animal is information regarding the diversity of the intestinal flora.

7. A disease prediction system as described in claim 5, wherein the time when the information on the animal's bad breath is acquired and the time when the information on the animal's intestinal bacteria is acquired are not separated by more than 180 days.

8. A disease prediction system as described in claim 5, wherein information regarding the animal's intestinal bacteria is stored in a database, and when the acquisition unit acquires information regarding the animal's bad breath, information regarding the intestinal bacteria of the same individual as the animal is called up from the database, and the prediction unit uses the information regarding the animal's bad breath and the information regarding the animal's intestinal bacteria to predict whether the animal will develop a disease within a specified period of time or whether it is currently suffering from a disease.

9. A method for predicting disease in an animal, comprising: a computer using information about the animal's malodor to predict whether the animal will develop a disease within a predetermined time period or whether the animal currently has a disease.

10. A health condition prediction system comprising: an acquisition unit that acquires halitosis information regarding an animal's halitosis; and a health condition prediction unit that uses the information regarding the halitosis to predict the health condition or future health condition of the animal.

11. The health condition prediction system according to claim 10, wherein the health condition prediction unit predicts the health condition or future health condition of the animal using information on the animal's bad breath and information on the animal's intestinal bacteria.

12. The health condition prediction system according to claim 11, wherein the information regarding the intestinal bacteria of the animal is information regarding the diversity of the intestinal microbiota.

13. A health condition prediction program that causes a computer to execute the steps of: acquiring halitosis information regarding an animal's halitosis; and predicting the health condition or future health condition of the animal using the information regarding the halitosis.

14. An information processing system comprising: an acquisition unit that acquires information regarding an animal's bad breath; and an output unit that outputs information encouraging care of the animal if the animal has bad breath based on the bad breath information.

15. An information processing program that causes a computer to execute the steps of: acquiring information about an animal's bad breath; and encouraging care of the animal if the animal has bad breath based on the bad breath information.

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