Old people health analysis system based on C4.5 decision tree algorithm

Abstract

The invention provides an old people health analysis system based on a C4.5 decision tree algorithm, and the system comprises a data collection terminal which is used for collecting the physiological health data of a user in real time; the data processing module is used for cleaning, standardizing and storing the data acquired by the data acquisition terminal; the algorithm analysis module is used for analyzing the processed health data by adopting a C4.5 decision tree algorithm, constructing a health data classification model, calculating the information gain ratio of each health data feature based on the information entropy and the information gain ratio, and selecting the optimal feature for splitting based on the information gain ratio so as to predict the health risk of the old people; and the personalized health intervention module is used for generating a personalized health intervention scheme for the user according to the health risk prediction result of the algorithm analysis module. According to the invention, comprehensive analysis and accurate intervention of the health data of the old people can be realized, and the efficiency and life quality of health management of the old people are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of health management systems, and in particular to an elderly health analysis system based on a C4.5 decision tree algorithm. Background Art

[0002] In modern society, as the aging problem becomes increasingly serious, the health management of the elderly has become an important issue that needs to be solved in the medical field. The acceleration of the global aging process has led to an increasing proportion of the elderly population. This trend has brought tremendous pressure to the traditional medical system, and also brought unprecedented opportunities and challenges to the field of medical and health management.

[0003] However, the current medical and health management system has many shortcomings in the health management of the elderly population.

[0004] First, incomplete data collection is a prominent problem. Traditional health management mainly relies on regular physical examinations for the elderly and health monitoring in hospitals, which has great limitations. The elderly often go to the hospital for examination only when they feel obvious discomfort, resulting in some potential health risks not being monitored and discovered in time. At the same time, the existing health data collection methods are single, often only focusing on some physiological indicators, while ignoring factors that have a significant impact on health, such as living habits and medical history, which seriously affects the comprehensiveness and accuracy of health data.

[0005] Secondly, low data processing efficiency is also a difficulty faced by the current health management system. With the development of information technology, a large amount of health data is collected and stored, but how to efficiently and intelligently process this data and extract valuable information has become an urgent problem to be solved. The existing health management system has limited data processing capabilities and cannot timely analyze large-scale and complex health data of the elderly, resulting in the inability to effectively predict and warn health risks, affecting the timeliness and accuracy of health management.

[0006] In addition, the lack of personalized health intervention programs is also a shortcoming of current health management methods. Most of the existing health management methods adopt a unified management model, lack personalized intervention programs, and cannot provide customized health guidance based on the specific health conditions of the elderly. This "one-size-fits-all" management method cannot fully meet the diverse health needs of the elderly and reduces the effectiveness of health management. At the same time, the existing health management system rarely combines the theory of traditional Chinese medicine to formulate dietary and health plans for the elderly, which makes health intervention measures lack scientificity and sustainability.

[0007] Finally, the irrational allocation of medical resources is also an important problem facing the current health management field. Due to the lack of an accurate health risk assessment mechanism, the allocation of medical resources is often too concentrated on the elderly whose health problems have already erupted, while for those elderly whose potential health risks have not been identified in time, there is less intervention of medical resources. The existing medical resource allocation model relies too much on manual judgment and cannot achieve automated and intelligent resource allocation, resulting in waste of medical resources and inefficient health management.

[0008] To sum up, existing technologies still have many shortcomings in the health management of the elderly, and breakthroughs are urgently needed through technological innovation. Summary of the invention

[0009] In response to the above-mentioned problems in the prior art, the purpose of the present invention is to provide a health analysis system for the elderly based on the C4.5 decision tree algorithm. By introducing intelligent data collection, processing and analysis technology, combined with personalized health intervention plans, it can achieve efficient collection, analysis and intervention of the health data of the elderly, significantly improve the effectiveness of health management for the elderly, and improve their quality of life.

[0010] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0011] A health analysis system for the elderly based on the C4.5 decision tree algorithm, comprising:

[0012] Data collection terminal, used to collect users' physiological health data in real time;

[0013] A data processing module, used for cleaning, standardizing and storing the data collected by the data collection terminal;

[0014] The algorithm analysis module uses the C4.5 decision tree algorithm to analyze the processed health data, build a health data classification model, calculate the information gain ratio of each health data feature based on information entropy and information gain ratio, and select the best feature for splitting based on the information gain ratio to predict the health risks of the elderly; wherein, according to the selected partitioning attribute, the data set is divided into several subsets, each subset corresponds to a child node; the C4.5 algorithm is recursively called for each child node, and the data set is continued to be divided according to the new partitioning attribute to generate a subtree of the child node; when the stop condition is met, the recursive process stops, and the current node becomes a leaf node, and the leaf node represents the prediction result of the health status;

[0015] The personalized health intervention module generates personalized health intervention plans for users based on the health risk prediction results of the algorithm analysis module.

[0016] According to a health analysis system for the elderly based on the C4.5 decision tree algorithm provided by the present invention, the data acquisition terminal comprises:

[0017] A smart wearable device with an embedded sensor for collecting the user's physiological index data in real time; the smart wearable device uploads the collected physiological index data to the cloud or local server for storage and processing via wireless communication;

[0018] Lifestyle monitoring equipment, which is used to record the user's daily living habits data through questionnaire surveys or in combination with smart home devices. The lifestyle monitoring equipment will record the lifestyle data together with the physiological index data to form a comprehensive health data set;

[0019] The medical history and medical record integration module is used to integrate the user's medical history and previous medical record data; the medical history and medical record data are obtained by connecting with the hospital's electronic medical record system, or collected by manual input, and stored together with real-time physiological data to build a complete health record of the user;

[0020] The various devices in the data collection terminal transmit data through a standardized interface; at the same time, the health data of each device and each user are distinguished by a unique identifier.

[0021] According to a health analysis system for the elderly based on the C4.5 decision tree algorithm provided by the present invention, the data collected by the data collection terminal is cleaned, standardized and stored, including:

[0022] Clean the original health data uploaded from the data collection terminal, automatically identify and eliminate abnormal data, redundant data and errors in the data collection process;

[0023] Fill in missing physiological indicator data; use interpolation or inference algorithms based on historical data to fill in missing values;

[0024] Standardize data from different devices and using different dimensions; convert data of different indicators to a unified scale through normalization processing methods;

[0025] The processed data is securely stored in the cloud database and stored in time series to facilitate subsequent data analysis and query; at the same time, the data is backed up regularly and encryption technology is used to protect sensitive information and prevent data leakage.

[0026] According to a health analysis system for the elderly based on the C4.5 decision tree algorithm provided by the present invention, in the process of constructing a health data classification model, the information gain ratio is used to evaluate the importance of each feature, which specifically includes the following steps:

[0027] Calculate feature entropy: For each feature, calculate its entropy value on the data set. This entropy value is used to reflect the uncertainty or confusion of the data.

[0028] Evaluate the information gain rate: Based on the calculated feature entropy value, further calculate the information gain rate of each feature. The information gain rate is an indicator to measure the effect of the feature on data classification. It indicates the uncertainty that can be reduced after using the feature for splitting;

[0029] Select the best features: According to the information gain rate, select the features that can minimize the uncertainty of the data as the splitting features of the current node to ensure the accuracy of the decision tree model in classification or prediction;

[0030] Recursive construction: For each subset, recursively perform feature selection and node splitting. The stopping conditions of recursive construction include: all features have been used up, the data purity of a node is high enough, that is, all samples belong to the same category, or the amount of data in the node is less than the preset threshold;

[0031] Leaf node generation: When the stopping condition is reached, the current node is set as a leaf node and a specific prediction category is assigned to it;

[0032] Construct a decision tree: Through the above steps, the optimal features are gradually selected for splitting, and a decision tree model is constructed as a health data classification model.

[0033] According to a health analysis system for the elderly based on the C4.5 decision tree algorithm provided by the present invention, for each feature, the entropy value thereof on the data set is calculated, including:

[0034] For a data set S, its entropy H(S) is calculated as follows:

[0035] H(S)=-∑(from i=1 to n)p_i*log(p_i)

[0036] Among them, p_i represents the proportion of samples belonging to category i, and n is the total number of categories; the higher the entropy, the greater the uncertainty of the data set.

[0037] Calculate the conditional entropy of features, including:

[0038] For a certain feature A, the conditional entropy H(S|A) represents the data uncertainty under the given conditions of feature A:

[0039] H(S|A)=∑(v∈Values(A))(|S_v| / |S|)*H(S_v)

[0040] Among them, S_v represents the data subset whose feature A takes the value of v, |S_v| is the subset size, and |S| is the size of the original data set.

[0041] According to a health analysis system for the elderly based on the C4.5 decision tree algorithm provided by the present invention, the information gain rate of each feature is calculated, including:

[0042] Calculate the reduced uncertainty after feature A selection, expressed as the following formula:

[0043] Gain(S,A)=H(S)-H(S|A)

[0044] Among them, the greater the information gain, the better the classification effect of feature A;

[0045] Calculate the information gain ratio, expressed as the following formula:

[0046] Gain_Ratio(S,A)=Gain(S,A) / H_A(S)

[0047] Among them, H_A(S) is the intrinsic entropy of feature A, expressed as the following formula:

[0048] H_A(S)=-∑(v∈Values(A))(|S_v| / |S|)*log2(|S_v| / |S|)

[0049] Among them, the inherent entropy is used to measure the discrete degree of feature values, and the information gain ratio is used to overcome the problem that information gain is biased towards multi-valued features.

[0050] According to the present invention, a health analysis system for the elderly based on the C4.5 decision tree algorithm is provided, which predicts the health risks of users through a health data classification model, including:

[0051] Data input and node judgment: The collected physiological health data, medical history and living habits information of the user are input into the root node of the decision tree model, wherein the root node is set based on the most important initial feature and is used to guide the data to the corresponding branch according to the feature of the input data;

[0052] Recursive splitting and path selection: When data flows through each node in the tree, the decision tree model recursively judges each feature and selects the branch where the data should continue to flow according to the feature value until the data reaches the leaf node;

[0053] Risk prediction of leaf nodes: When data reaches the leaf nodes of the decision tree, the final prediction result of the user's health risk is given based on all the characteristic conditions on the data flow path, where the leaf nodes represent specific health states;

[0054] Warning and suggestions: If the prediction results indicate that the user is in a high-risk health state, a health assessment report will be automatically generated and targeted intervention suggestions will be provided.

[0055] According to a health analysis system for the elderly based on the C4.5 decision tree algorithm provided by the present invention, pre-pruning and post-pruning techniques are also used to prevent overfitting of the decision tree, specifically including the following steps:

[0056] Application of pre-pruning technology: During the growth of the decision tree, stop conditions are set. When the decision tree reaches a preset depth, the number of samples in the node is lower than the threshold, or the information gain rate is lower than the predetermined standard, the tree is stopped from further splitting to control the complexity and size of the tree.

[0057] Application of post-pruning technology: After the decision tree model is built, the model is verified using a test data set to evaluate the contribution of each node and branch to the model performance. Branches and leaves that do not significantly improve the generalization ability of the model or cause the model to overfit are pruned to simplify the model structure and improve the generalization ability of the model.

[0058] According to the present invention, a health analysis system for the elderly based on the C4.5 decision tree algorithm further includes:

[0059] The split stop evaluation module is used to evaluate whether the split can significantly improve the prediction ability of the model each time a node splits;

[0060] The split stop evaluation module specifically performs the following steps:

[0061] Determine whether the number of samples of the current node is less than the preset minimum sample number threshold. If so, stop splitting to avoid overfitting splitting when the sample size is very small;

[0062] If the number of samples of the current node is not less than the preset minimum number of samples, the information gain after the split is further calculated, and it is determined whether the information gain is less than the preset information gain threshold. If so, it is considered that the contribution of the split to the model effect is insufficient, and the split is stopped;

[0063] In the elderly health analysis system, when the samples contained in a certain node are sufficient to show that these elderly people have lower health risks, if further splitting cannot significantly improve the distinction of health status, that is, the information gain after splitting is less than the information gain threshold, then the split stop evaluation module stops the splitting here to avoid the model being too complicated.

[0064] According to a health analysis system for the elderly based on the C4.5 decision tree algorithm provided by the present invention, after dividing the physiological health data into a training set and a test set, the decision tree model is trained on the training set by iteratively adjusting the splitting conditions and pruning rules of the model, specifically including:

[0065] Based on the training results of the current training set, analyze the prediction accuracy of the model on the test set;

[0066] According to the prediction accuracy on the test set, determine the splitting condition parameters and / or pruning rule parameters that need to be adjusted;

[0067] Adjusting the splitting condition parameters and / or pruning rule parameters to form a new decision tree model;

[0068] Retrain the training set using the adjusted new decision tree model and validate it again on the test set;

[0069] Repeat the above steps until the prediction accuracy on the test set reaches the preset optimization standard or no longer improves significantly.

[0070] It can be seen that compared with the prior art, the present invention has the following beneficial effects:

[0071] 1. Comprehensively improve the efficiency and accuracy of health management: The present invention collects multi-dimensional health data of the elderly in real time and continuously through smart wearable devices and a variety of health monitoring tools, ensuring the comprehensiveness and real-time nature of the data, greatly improving the efficiency of health management. The present invention uses the C4.5 decision tree algorithm to efficiently process and deeply analyze massive amounts of health data, improving the accuracy of health data classification and prediction, making health management more scientific and precise.

[0072] 2. Personalized health intervention: The present invention can generate highly personalized health intervention plans based on the specific health data of each elderly person, including dietary matching, exercise planning, and medication reminders, etc., to meet the different health needs of the elderly. This personalized intervention method not only improves the quality of life of the elderly, but also effectively reduces the risk of disease and delays the deterioration of health problems.

[0073] 3. Improve the accuracy and predictability of health risk identification: This invention combines historical health data with real-time monitoring data and uses the predictive ability of the decision tree algorithm to accurately assess the health risk level of the elderly and issue health warnings in advance. This predictive health management method effectively prevents the deterioration of health problems, helps the elderly take intervention measures early, and avoids the occurrence of sudden diseases.

[0074] 4. Optimize the allocation of medical resources and reduce medical costs: Through intelligent health data analysis and health risk assessment, the present invention can reasonably allocate medical resources and avoid waste or shortage of resources. The effective implementation of personalized health intervention programs reduces the occurrence of health problems, reduces the demand for medical resources, and thus reduces medical costs.

[0075] 5. Improve the effectiveness of health management: Real-time health monitoring and personalized intervention suggestions help the elderly maintain a good living condition and extend their healthy life expectancy. The intelligent design of the system facilitates the elderly to manage their own health and increases their enthusiasm for health management.

[0076] 6. Promote the development of intelligent health management technology: The successful application of this invention verifies the application potential of big data technology and machine learning algorithms in health management, laying the foundation for the development of future intelligent health management technology. The scalability and adaptability of the system enable it to be applied to a wider range of health management scenarios, promoting technological progress in the field of intelligent health management.

[0077] In summary, the present invention provides an innovative and practical solution for the health management of the elderly through comprehensive data collection, efficient data processing, personalized intervention plan generation, accurate health risk identification and optimized medical resource allocation. It not only effectively improves the health level of the elderly, but also reduces medical costs and improves the utilization efficiency of medical resources. At the same time, the wide application of the present invention will greatly promote the development of intelligent health management technology and make positive contributions to the health protection and quality of life improvement of the elderly.

[0078] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a schematic diagram of an embodiment of an elderly health analysis system based on a C4.5 decision tree algorithm of the present invention.

[0080] Figure 2 It is a flow diagram of an embodiment of the health analysis system for the elderly based on the C4.5 decision tree algorithm of the present invention.

[0081] Figure 3 It is a schematic diagram of the principle of the C4.5 decision tree algorithm in an embodiment of the elderly health analysis system based on the C4.5 decision tree algorithm of the present invention. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0083] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0084] See also Figures 1 to 3 , this embodiment provides a health analysis system for the elderly based on the C4.5 decision tree algorithm, including:

[0085] Data collection terminal, used to collect users' physiological health data in real time. Among them, the collected data types include but are not limited to: heart rate: collect the heart rate data of the elderly in real time through smart bracelets to monitor the health status of the heart; blood pressure: use smart blood pressure monitors to collect blood pressure data of the elderly on a regular basis every day to assess the cardiovascular health of the elderly; blood sugar: smart blood glucose meters regularly collect blood sugar levels of the elderly to help manage metabolic diseases such as diabetes; sleep quality: smart bracelets or sleep monitoring devices record parameters such as sleep duration and deep sleep time of the elderly to assess sleep health; body temperature: body temperature monitoring devices collect body temperature data in real time to detect potential health problems such as fever. These collection devices are easy to install, suitable for home environments, and can collect health data of the elderly in a long-term and stable manner. The device is bound to the system through a unique identifier (such as device ID or user ID) to ensure that the data can accurately correspond to each elderly person.

[0086] The data processing module is used to clean, standardize and store the data collected by the data acquisition terminal.

[0087] The algorithm analysis module uses the C4.5 decision tree algorithm to analyze the processed health data, builds a health data classification model, calculates the information gain ratio of each health data feature based on information entropy and information gain ratio, and selects the best feature for splitting based on the information gain ratio to predict the health risks of the elderly; wherein, according to the selected partitioning attribute, the data set is divided into several subsets, each subset corresponds to a child node; the C4.5 algorithm is recursively called for each child node, and the data set is continued to be divided according to the new partitioning attribute to generate a subtree of the child node; when the stopping condition is met, the recursive process stops, and the current node becomes a leaf node, and the leaf node represents the predicted result of the health status.

[0088] The personalized health intervention module generates personalized health intervention plans for users based on the health risk prediction results of the algorithm analysis module.

[0089] Therefore, this embodiment proposes an intelligent elderly health analysis system based on big data technology and decision tree algorithm, which realizes real-time collection, processing and personalized health intervention of elderly health data by integrating various intelligent hardware, data processing technology and artificial intelligence algorithm. The system is composed of multiple mutually coordinated components, including data acquisition terminal, data processing module, algorithm analysis module, personalized health intervention module, etc. These modules ensure that the system can complete health management tasks efficiently and accurately through close connection relationship and efficient data flow.

[0090] The system of this embodiment provides a real-time, comprehensive and intelligent health management solution for the elderly through intelligent hardware devices and background analysis algorithms. Through big data collection, preprocessing, decision tree algorithm analysis and the generation of personalized health intervention suggestions, it realizes a comprehensive analysis of the health data of the elderly, and can timely predict potential health risks and provide targeted intervention plans. Among them, the main working principle of the system is based on the order of data flow: first, the health data of the elderly is collected by intelligent hardware devices, and the data is transmitted to the central processing unit through the wireless network for data cleaning and preprocessing. Then, the C4.5 decision tree algorithm analyzes the health data and predicts health risks. Finally, the system generates a personalized health intervention plan and pushes it to the user in real time.

[0091] In this embodiment, the data acquisition terminal is the first line of defense of the system, responsible for collecting the health data of the elderly in real time. It collects various physiological indicators of the elderly through smart wearable devices and sensors, and transmits these data to the data processing module in a timely manner. Among them, the data acquisition terminal specifically includes:

[0092] Smart wearable devices, such as smart bracelets and smart watches, are equipped with embedded sensors to collect users' physiological indicator data in real time, such as the elderly's heart rate, blood pressure, blood sugar, body temperature and other physiological indicators, and upload the collected physiological indicator data to the cloud or local server for storage and processing through wireless communication methods such as Bluetooth and WiFi.

[0093] Lifestyle monitoring equipment is used to record users' daily living habits data, such as diet, exercise, work and rest, through questionnaires or in combination with smart home devices. Lifestyle monitoring equipment will record the living habits data together with the physiological index data to form a comprehensive health data set, which provides a basis for subsequent personalized health management.

[0094] The medical history and medical record integration module is used to integrate the user's medical history and previous medical record data, such as chronic diseases, medication records, etc.; these data can be obtained by connecting to the hospital's electronic medical record system, or collected through manual input, and stored together with real-time physiological data to build a complete health profile for the user.

[0095] Among them, the various devices in the data collection terminal transmit data through standardized interfaces. At the same time, the health data of each device and each user are distinguished from different users through unique identifiers (such as device ID, user ID), ensuring that the health data of each user can be accurately processed.

[0096] In this embodiment, the data processing module is the core processing unit of the system, which is mainly responsible for cleaning, standardizing and storing the raw data uploaded from the data acquisition terminal. Its main functions include:

[0097] Data cleaning: Abnormal data may appear during the data collection process (such as abnormal heart rate and blood pressure readings caused by equipment errors or external interference). In the data processing module, the original health data uploaded from the data collection terminal is first cleaned to automatically identify and eliminate abnormal data, redundant data, and errors in the data collection process. For example, if the heart rate data collected by the smart bracelet suddenly shows an extreme value (for example, the heart rate exceeds 200 beats per minute), the system will automatically identify it and regard it as an abnormal value and remove it from the data set.

[0098] Fill in missing physiological indicator data; when the data collection terminal fails to successfully collect certain data due to signal problems or equipment failures, in order to ensure the integrity of the data, the system will fill in the missing data, usually using interpolation methods or inference algorithms based on historical data to fill in the missing physiological indicators to ensure the continuity and integrity of the data. For example, if the blood pressure data of an elderly person is not successfully recorded at a certain moment, the system can infer the blood pressure at that moment based on previous health data.

[0099] Data normalization and standardization: Health data from different sources (such as blood pressure, blood sugar, etc.) may use different dimensions. To facilitate subsequent analysis, the system standardizes data from different devices and using different dimensions; through normalization, data of different indicators are converted to a unified scale. Since data from different devices may use different dimensions, the system standardizes these data. For example, the unit of blood pressure is millimeter of mercury (mmHg), and the unit of blood sugar is milligram per deciliter (mg / dL). Through normalization (such as Zscore normalization or MinMax normalization), data of different indicators are converted to a unified scale, and all data are converted to between 01 to facilitate subsequent algorithm analysis.

[0100] Data storage and backup: The processed data is securely stored in the cloud database and stored in time series to facilitate subsequent data analysis and query; at the same time, to ensure data security, the data is backed up regularly and encryption technology is used to protect sensitive information and prevent data leakage.

[0101] It can be seen that the data processing module is the foundation of the entire system. It ensures the stability and consistency of data quality and provides high-quality data input for subsequent algorithm analysis.

[0102] In this embodiment, the algorithm analysis module is the intelligent core of the system, relying on the C4.5 decision tree algorithm, responsible for in-depth analysis and prediction of the health data of the elderly. Its main functions include:

[0103] Construction of decision tree model: The system constructs a classification model for health data through the C4.5 decision tree algorithm. The algorithm generates a tree structure model by recursively segmenting the data set. Each node represents a feature (such as blood pressure or heart rate), each branch represents a different value range of the feature, and the leaf node represents the predicted result of the health status (such as healthy, mild risk, high risk). Among them, in the blood pressure feature, the system may set multiple intervals (such as 120140mmHg is normal, 140160mmHg is mild hypertension, and 160mmHg and above is high risk). According to the current blood pressure level of the elderly, the model predicts their health risk level. For example, if an elderly person has high blood pressure and a history of diabetes, the system may predict that the elderly person has a higher risk of heart disease.

[0104] Information gain and feature selection: During the construction of the decision tree, the system uses the information gain ratio to evaluate the importance of each feature. The information gain rate is an indicator of the effect of a feature on data classification. The system calculates the entropy value of each feature and selects those features that can minimize uncertainty for splitting, thereby ensuring the accuracy of the decision tree model. It can be seen that when building a decision tree model, the system first evaluates each health feature and calculates its information gain rate. Information gain refers to the contribution of a feature to the classification task and is used to select the optimal feature for data splitting. For example, when processing multiple health features such as blood pressure, heart rate, and blood sugar, the system selects the feature that can best distinguish between healthy and unhealthy states to split the data set based on the information gain rate of each feature.

[0105] Health risk prediction: Through the decision tree model, the system can accurately predict the health risks of the elderly. The system can not only predict the current health status, but also predict future health risks based on the trend of historical data. For example, the system can predict whether the elderly are at risk of hypertension in the next few months based on the blood pressure fluctuations of the elderly in the past few months. It can be seen that the system will predict whether the elderly are at risk of cardiovascular disease based on the current physiological data such as heart rate, blood pressure, blood sugar level, etc., combined with historical data, and issue warnings in time.

[0106] Pruning technology prevents overfitting: To prevent the decision tree from overfitting, the system uses pre-pruning and post-pruning technology. Pre-pruning technology sets the stopping condition of the decision tree and stops splitting when the tree grows to a certain depth; post-pruning technology prunes unnecessary branches and leaves after the model is built, based on the performance of the model on the test data set, thereby improving the generalization ability of the model.

[0107] Model optimization and verification: The system optimizes the decision tree model through cross-validation technology. The specific method is to divide the health data into a training set and a test set. After the model is trained on the training set, it is verified on the test set, and the accuracy of the prediction is improved by adjusting the model parameters (such as splitting conditions and pruning rules).

[0108] Through the algorithm analysis module, the system can conduct comprehensive and accurate analysis and prediction of the health status of the elderly, providing a reliable basis for subsequent personalized health interventions.

[0109] In the process of building a health data classification model, the information gain ratio is used to evaluate the importance of each feature, which includes the following steps:

[0110] Calculate feature entropy: For each feature, calculate its entropy value on the data set. This entropy value is used to reflect the uncertainty or confusion of the data.

[0111] Evaluate the information gain rate: Based on the calculated feature entropy value, further calculate the information gain rate of each feature. The information gain rate is an indicator to measure the effect of the feature on data classification. It indicates the uncertainty that can be reduced after using the feature for splitting;

[0112] Select the best features: According to the information gain rate, select the features that can minimize the uncertainty of the data as the splitting features of the current node to ensure the accuracy of the decision tree model in classification or prediction;

[0113] Recursive construction: For each subset, recursively perform feature selection and node splitting. The stopping conditions of recursive construction include: all features have been used up, the data purity of a node is high enough, that is, all samples belong to the same category, or the amount of data in the node is less than the preset threshold;

[0114] Leaf node generation: When the stopping condition is reached, the current node is set as a leaf node and a specific prediction category is assigned to it;

[0115] Construct a decision tree: Through the above steps, the optimal features are gradually selected for splitting, and a decision tree model is constructed as a health data classification model.

[0116] In this embodiment, pre-pruning and post-pruning techniques are also used to prevent overfitting of the decision tree, which specifically includes the following steps:

[0117] Application of pre-pruning technology: During the growth of the decision tree, stop conditions are set. When the decision tree reaches a preset depth, the number of samples in the node is lower than the threshold, or the information gain rate is lower than the predetermined standard, the tree is stopped from further splitting to control the complexity and size of the tree.

[0118] Application of post-pruning technology: After the decision tree model is built, the model is verified using a test data set to evaluate the contribution of each node and branch to the model performance. Branches and leaves that do not significantly improve the generalization ability of the model or cause the model to overfit are pruned to simplify the model structure and improve the generalization ability of the model.

[0119] In this embodiment, it also includes:

[0120] The split stop evaluation module is used to evaluate whether the split can significantly improve the prediction ability of the model each time a node splits;

[0121] The split stop evaluation module specifically performs the following steps:

[0122] Determine whether the number of samples of the current node is less than the preset minimum sample number threshold. If so, stop splitting to avoid overfitting splitting when the sample size is very small;

[0123] If the number of samples of the current node is not less than the preset minimum number of samples, the information gain after the split is further calculated, and it is determined whether the information gain is less than the preset information gain threshold. If so, it is considered that the contribution of the split to the model effect is insufficient, and the split is stopped;

[0124] In the elderly health analysis system, when the samples contained in a certain node are sufficient to show that these elderly people have lower health risks, if further splitting cannot significantly improve the distinction of health status, that is, the information gain after splitting is less than the information gain threshold, the split stop evaluation module stops the splitting here to avoid the model being too complicated.

[0125] Specifically, in the process of building a decision tree, feature selection is the key first step. The system will select the most effective feature to distinguish the data from all candidate features for splitting at the current node.

[0126] The C4.5 algorithm measures the contribution of each feature to the classification effect by calculating the information gain ratio of each feature. The information gain ratio is based on the information gain, divided by the "intrinsic entropy" of the feature to avoid feature bias. For example, the information gain of feature A (such as blood pressure) is calculated as the weighted entropy of each part after the split is subtracted from the overall entropy of the data set. The larger the information gain ratio, the better the feature can reduce the uncertainty of the data set, and thus this feature is preferred for splitting.

[0127] Once the best feature for the current node is determined, the system will split the data set based on that feature. For example, if "blood pressure" is selected as the split feature, the node will divide the data set into different subsets based on different values ​​of blood pressure (such as "normal blood pressure", "mild hypertension", "high risk").

[0128] These subsets form different branches of the decision tree, each branch corresponding to a different health data feature. In this way, the decision tree can continuously refine the data into more specific classifications until each leaf node contains only samples of a specific health state.

[0129] For each subset, the system recursively performs the process of feature selection and node splitting. The stopping conditions of the recursive construction include: all features have been used up, the data purity of a node is high enough (that is, all samples belong to the same category), or the amount of data in the node is less than the preset threshold.

[0130] In addition, to improve efficiency, the system may stop further splitting when certain thresholds are met (such as too few node samples) to avoid generating an overly complex tree.

[0131] When the stopping condition is reached, the current node is set as a leaf node and a specific prediction category is assigned to it. For example, if most samples in a node have high blood pressure and a history of diabetes, the system predicts the node as a "high heart disease risk" state.

[0132] The generation of a leaf node not only marks the end point of the data set classification, but also determines the prediction result of the user's health status under this path. Each leaf node represents a final health classification result, such as "healthy", "moderate risk", "high risk", etc.

[0133] In order to prevent overfitting (i.e. the decision tree is too complex, resulting in overfitting of the training data and reduced generalization ability for new data), the system will perform pruning after the decision tree is built. There are two types of pruning: pre-pruning and post-pruning.

[0134] Pre-pruning is to stop some unnecessary splits in advance by setting conditions during the tree generation process. For example, if further splitting cannot significantly improve the information gain of the model, the system will stop splitting and set the current node as a leaf node; post-pruning is to re-evaluate each leaf node after the decision tree is fully generated, and prune the child nodes that do not contribute much to the classification accuracy. This can simplify the model structure, reduce the impact of noise, and improve the generalization ability of the model.

[0135] To ensure the accuracy and robustness of the model, the system will use the cross-validation method to verify the model. Specifically, the data set is divided into a training set and a test set, the model is trained with the training set, and then the model is verified with the test set. During the cross-validation process, the system may split the data set multiple times and train multiple models to evaluate the effects of different parameter configurations. In this way, the system can find the optimal decision tree structure and avoid overfitting or underfitting of the model.

[0136] In addition, the system will adjust some important parameters (such as the minimum number of samples for splitting nodes, the threshold of information gain ratio, etc.) to further improve the prediction accuracy and stability of the model.

[0137] Finally, based on the constructed decision tree model, the system can intelligently analyze the health data of the elderly and give health status predictions. For example, the system will predict whether the elderly are at high risk of cardiovascular disease based on their current physiological indicators such as heart rate, blood pressure, and blood sugar levels, combined with medical history data. The system can also generate personalized health recommendations for users, such as daily activity adjustments, dietary recommendations, regular health checks, etc. For example, if an elderly person has always had high blood pressure and a history of diabetes, the decision tree model may classify him or her as a "high risk of heart disease" and promptly remind the user to take appropriate measures to prevent health deterioration.

[0138] Through the above detailed steps, the C4.5 decision tree algorithm can systematically build a classification model for health data, recursively select the optimal features to split the data set, and thus generate a health prediction model with high accuracy and interpretability. The final result can not only provide personalized health status assessment for the elderly group, but also support doctors and health managers to conduct more scientific and effective interventions and resource allocation.

[0139] Furthermore, for each feature, the entropy value of the feature on the data set is calculated, including:

[0140] For a data set S, its entropy H(S) is calculated as follows:

[0141] H(S)=-∑(from i=1 to n)p_i*log(p_i)

[0142] Among them, p_i represents the proportion of samples belonging to category i, and n is the total number of categories; the higher the entropy, the greater the uncertainty of the data set.

[0143] Furthermore, the conditional entropy of the features is calculated, including:

[0144] For a certain feature A, the conditional entropy H(S|A) represents the data uncertainty under the given conditions of feature A:

[0145] H(S|A)=∑(v∈Values(A))(|S_v| / |S|)*H(S_v)

[0146] Among them, S_v represents the data subset whose feature A takes the value of v, |S_v| is the subset size, and |S| is the size of the original data set.

[0147] Furthermore, the information gain rate of each feature is calculated, including:

[0148] Calculate the reduced uncertainty after feature A selection, expressed as the following formula:

[0149] Gain(S,A)=H(S)-H(S|A)

[0150] Among them, the greater the information gain, the better the classification effect of feature A;

[0151] Calculate the information gain ratio, expressed as the following formula:

[0152] Gain_Ratio(S,A)=Gain(S,A) / H_A(S)

[0153] Among them, H_A(S) is the intrinsic entropy of feature A, expressed as the following formula:

[0154] H_A(S)=-∑(v∈Values(A))(|S_v| / |S|)*log2(|S_v| / |S|)

[0155] Among them, the inherent entropy is used to measure the discrete degree of feature values, and the information gain ratio is used to overcome the problem that information gain is biased towards multi-valued features.

[0156] It can be seen that by calculating the information gain ratio of each feature and selecting the feature with the largest information gain ratio for node splitting, the C4.5 algorithm selects the feature that can minimize uncertainty and ensures the accuracy and generalization ability of the decision tree.

[0157] In specific applications, the health data classification model is used to predict the user's health risks, including:

[0158] Data input and node judgment: The collected user's physiological health data (such as heart rate, blood pressure, blood sugar level, etc.), medical history and living habits information are input into the root node of the decision tree model, where the root node is set based on the most important initial feature (such as blood pressure) and is used to guide the data to the corresponding branch according to the feature of the input data. For example, if the current blood pressure value exceeds a certain critical value, the system will select the branch representing high blood pressure and proceed to the next step of judgment.

[0159] Recursive splitting and path selection: When data flows through each node in the tree, the decision tree model recursively judges each feature (such as heart rate, blood sugar, past medical history, etc.), and selects the branch where the data should continue to flow based on the feature value until the data reaches the leaf node and selects the most suitable branch. For example, if the elderly have high blood pressure and a long history of diabetes, the model will use this information to conduct in-depth analysis layer by layer, and then select a path that matches this health condition.

[0160] Risk prediction of leaf nodes: When data reaches the leaf nodes of the decision tree, the final prediction result of the user's health risk is given based on all the characteristic conditions on the data flow path, where the leaf nodes represent specific health states. For example, "low risk", "moderate risk" or "high cardiovascular disease risk". This prediction result is based on known patterns in the training data, so it has a high reference value.

[0161] Warning and suggestions: If the prediction results indicate that the user is in a high-risk health state, a health assessment report will be automatically generated, detailing the elderly’s current health status, potential health risks, and providing targeted intervention suggestions. For example, the system may recommend that the elderly monitor their blood pressure regularly, increase their exercise, change their eating habits, or go to the hospital for further professional consultation.

[0162] In this way, the decision tree model can make detailed and accurate predictions about the health risks of the elderly based on multi-dimensional data, helping the elderly and their health managers to identify potential health risks as early as possible and take preventive measures, thereby effectively reducing the probability of health problems.

[0163] After the physiological health data is divided into a training set and a test set, the decision tree model is trained on the training set by iteratively adjusting the splitting conditions and pruning rules of the model, including:

[0164] Based on the training results of the current training set, analyze the prediction accuracy of the model on the test set; determine the splitting condition parameters and / or pruning rule parameters that need to be adjusted based on the prediction accuracy on the test set; adjust the splitting condition parameters and / or pruning rule parameters to form a new decision tree model; use the adjusted new decision tree model to retrain the training set and verify it again on the test set; repeat the above steps until the prediction accuracy on the test set reaches the preset optimization standard or no longer improves significantly.

[0165] Specifically, to prevent the decision tree from overfitting, the system of this embodiment adopts pre-pruning and post-pruning technology to ensure that the model remains efficient and has good generalization ability when processing health data. The following is the implementation principle of pruning technology in a specific decision tree model:

[0166] Pre-pruning technology: Pre-pruning sets stopping conditions during the growth of the decision tree to terminate certain split operations in advance to avoid complexity caused by excessive tree growth. Specifically, each time a node splits, the system will evaluate whether the split can significantly improve the model's predictive ability. Stopping conditions usually include:

[0167] Minimum sample number threshold: If the number of samples of the current node is less than a preset threshold (for example, 10 samples), stop splitting. This is to avoid overfitting splitting when the sample size is very small.

[0168] Information gain threshold: If the information gain after splitting is less than a certain threshold, it is considered that the split does not contribute enough to the model effect, and the split is stopped. For example, if the split of a feature only brings an information gain increment of 0.01, and this increment is not enough to significantly reduce entropy, the system will directly treat the node as a leaf node.

[0169] In the elderly health management system, assuming that the samples contained in a certain node are sufficient to show that these elderly people have lower health risks, if further splitting cannot significantly improve the distinction of health status, the system will stop splitting here to avoid the model being too complicated.

[0170] Post-pruning technology: After the entire decision tree is built, post-pruning is to remove unnecessary branches by re-evaluating the entire tree to reduce the risk of overfitting. The specific implementation steps are as follows:

[0171] Validation set evaluation: The system applies the decision tree model to an independent validation data set to evaluate the contribution of each subtree to the prediction. If the removal of a subtree does not significantly reduce the accuracy of the model on the validation set, it is pruned. For example, when evaluating health data for the elderly, if a subtree (such as the combined branch of "blood sugar level" and "eating habits") contributes little to the final health risk prediction, it can be removed to simplify the model.

[0172] Leaf node merging: For some leaf nodes that contain only a few samples, if merging them with adjacent nodes can improve the performance of the model on the validation data, the system will merge these leaf nodes. This can effectively reduce the complexity of the model and improve its generalization ability to new data.

[0173] In practical applications, such as health risk assessment for a certain elderly population, the system first builds a complete decision tree model, which may contain very detailed branches (such as combinations of different blood sugar and blood pressure values). During the pre-pruning process, if it is found that a branch brings little gain in the split of the feature value, the system will stop the continued growth of the branch to avoid generating complex nodes that do not improve the results much.

[0174] After the model is fully built, the system uses post-pruning technology to check the performance of the model through the validation set. For example, if removing some small branches (such as branches based on a rare lifestyle habit) does not reduce the model's prediction accuracy for health risks, the system will prune these branches to improve the model's generalization ability.

[0175] Through the above pruning technology, the system can effectively prevent the overfitting of the decision tree model, ensure that the model structure is simple and accurate, and has strong generalization ability. This is especially important when processing health data of the elderly, because the health characteristics of the elderly are diverse and complex. Pruning can help the model better cope with different health risk scenarios and provide more robust prediction results.

[0176] In this embodiment, the personalized health intervention module is the output part of the system, which is responsible for converting the health risk prediction results of the algorithm analysis module into personalized health intervention plans and pushing them to users in real time. The main functions of this module include:

[0177] Health assessment report generation: The system automatically generates a health assessment report that includes health status analysis, health risk assessment, and personalized recommendations based on the prediction results of the decision tree model. The report includes current physiological status analysis (such as whether heart rate, blood pressure, and blood sugar are within the normal range), potential health risks (such as the risk of cardiovascular disease, diabetes, etc.), and health management recommendations. For example, the system will indicate whether the elderly person's current blood pressure exceeds the standard, whether the heart rate is abnormal, and provide detailed instructions.

[0178] Personalized intervention plan: Based on the analysis results in the health assessment report, the system will tailor personalized health intervention plans for the elderly. These plans include but are not limited to exercise recommendations, dietary guidance, and medication reminders. For example, for elderly people with high blood sugar, the system will recommend reducing the intake of sugary foods and provide a suitable diet. In addition, the system also combines traditional Chinese medicine theory to recommend traditional Chinese medicine dietary plans suitable for the physical condition of the elderly to help improve health. For example, the system will recommend users to perform specific exercises (such as walking for 30 minutes a day) or recommend traditional Chinese medicine diets (such as diets with blood sugar-lowering functions) to regulate their health status. Personalized intervention plans will be adjusted in real time according to the actual situation of the user to ensure the effectiveness and scientificity of the intervention measures.

[0179] Health monitoring and feedback mechanism: The system monitors changes in the health data of the elderly in real time and dynamically adjusts the intervention plan based on the latest data. For example, when the system finds that the blood sugar level of the elderly has fluctuated greatly in the recent period, it will automatically adjust the dietary recommendations and remind the user to seek medical advice in a timely manner, or after the system detects that the blood sugar level of the elderly has stabilized, it may recommend a gradual reduction in the dosage of medication; if the effect of some interventions is not obvious, the system will prompt the user to return for a follow-up visit or change the intervention plan in a timely manner. Users can view health recommendations through mobile applications or other terminal devices and provide feedback on the intervention effect. The system will continuously optimize the intervention measures based on user feedback.

[0180] Health reminders and medication management: For elderly people who need long-term medication, the system will provide medication reminders based on the doctor's advice to ensure that users take medications on time and avoid missing or improper use of medications. At the same time, the system can regularly adjust medication dosages or recommend follow-up visits based on the user's health data. For health indicators that need to be monitored regularly (such as blood pressure, blood sugar, etc.), the system will also send regular reminders to help users manage their daily health.

[0181] The personalized health intervention module is the interactive interface between the system and the user. Through intelligent health management solutions, the system can provide customized health guidance for each elderly person and continuously optimize the management effect through real-time feedback mechanisms.

[0182] In practical applications, the system of this embodiment demonstrates how to perform health management for elderly people with hypertension through the following specific examples:

[0183] 1. Data collection: The elderly wear smart bracelets and smart blood pressure monitors. The system collects their heart rate and blood pressure data regularly every day and transmits them wirelessly to the background system.

[0184] 2. Data processing: The system first cleans and standardizes the collected data. For example, if the blood pressure data on a certain day has an abnormal reading (such as lower than 90 / 60mmHg or higher than 180 / 110mmHg), the system will mark it as abnormal data and remove it. After data cleaning, the system standardizes all blood pressure data to between 0 and 1 for subsequent analysis.

[0185] 3. Algorithm analysis: The system uses the C4.5 decision tree algorithm to analyze blood pressure data. Based on the elderly's historical blood pressure data, the decision tree model classifies them into three categories: "normal blood pressure", "mild hypertension" and "high-risk hypertension". Assuming that the elderly's blood pressure remains between 140 and 160 mmHg most of the time, the model predicts that they may develop hypertension in the next few months.

[0186] 4. Health risk prediction: By analyzing the blood pressure fluctuations of the elderly in the past few months, the system predicts the health risks he may face in the future and generates a health assessment report. The report points out that the elderly person's blood pressure fluctuates greatly and has a high risk of cardiovascular disease. It is recommended to increase the frequency of daily blood pressure monitoring and conduct regular health checks.

[0187] 5. Personalized intervention plan: Based on the health assessment report, the system provides personalized health intervention suggestions for the elderly, including 30 minutes of aerobic exercise every day, dietary suggestions to reduce salt intake, and a traditional Chinese medicine diet plan to lower blood pressure. At the same time, the system sets a medication reminder function to ensure that the elderly take antihypertensive drugs on time.

[0188] 6. Health monitoring and feedback: The system continuously tracks the effectiveness of intervention programs through daily data collection and analysis. If it is found that the elderly's blood pressure is well controlled, the system will gradually reduce the frequency of health monitoring; if the blood pressure continues to rise, the system will automatically prompt the user to go to the hospital for a follow-up visit.

[0189] In practical applications, the system of this embodiment demonstrates how to perform health management for elderly people with diabetes through the following specific examples:

[0190] 1. Data collection: The elderly use smart blood glucose meters to collect their blood glucose levels every day. The system records the blood glucose data and uploads it to the server together with other physiological indicators (such as body temperature and heart rate).

[0191] 2. Data processing: The system first cleans and normalizes the blood sugar data. Since the blood sugar levels of different individuals vary greatly, the system will set different blood sugar ranges according to the individual characteristics of the elderly. If the blood sugar reading on a certain day exceeds the preset range, the system will issue a warning.

[0192] 3. Algorithm analysis: The system uses the C4.5 decision tree algorithm to analyze the elderly person’s blood sugar level and combines it with other health data (such as BMI, medical history, etc.) to predict whether they are at risk of developing diabetic complications. The model classifies the user’s health status into three levels: “well controlled”, “needs attention”, and “high risk”.

[0193] 4. Health risk prediction: The system generates a health assessment report, pointing out the blood sugar fluctuations of the elderly, and reminding them that their blood sugar control is not good recently, and there may be a risk of complications. The system recommends that users adjust their diet and increase the frequency of blood sugar monitoring.

[0194] 5. Personalized intervention plan: The system recommends a personalized dietary plan for the elderly to reduce the intake of high-sugar foods and increase foods rich in fiber. At the same time, the system also recommends that the elderly do moderate aerobic exercise every day and provides a medication reminder function.

[0195] 6. Health monitoring and feedback: The system tracks changes in blood sugar data every day and adjusts dietary and exercise recommendations based on the latest data. If blood sugar levels gradually return to normal, the system will reduce the frequency of monitoring; if blood sugar fluctuates too much, the system will prompt the user to seek medical attention in time.

[0196] Of course, in addition to the health management of the elderly, the system of the present invention can also be extended to the health management of other special groups, such as pregnant women, patients with chronic diseases, etc. By integrating different types of health data collection devices and targeted health intervention algorithms, the system can provide personalized health management services for different groups of people. For example, health management of pregnant women: by monitoring the weight, blood sugar, blood pressure and other data of pregnant women, the system can promptly detect early signs of pregnancy complications and provide personalized diet and exercise recommendations during pregnancy; management of patients with chronic diseases: the system can help patients with chronic diseases (such as asthma, chronic obstructive pulmonary disease, etc.) manage their daily health conditions, promptly identify the risk of worsening of the disease, and provide corresponding treatment recommendations.

[0197] In summary, the present invention realizes comprehensive management of the health data of the elderly by integrating intelligent hardware, big data processing technology and decision tree algorithm. The system can collect and analyze the health data of the elderly in real time and generate personalized health intervention plans. Through the illustration of a series of actual cases, the present invention provides an efficient and intelligent solution for the field of health management, greatly improving the health management efficiency and quality of life of the elderly population, and has significant technical advantages and application value in many aspects, which are specifically reflected in the following aspects:

[0198] 1. Comprehensive data collection and health monitoring: Existing health management systems usually rely on regular physical examinations or manual entry for data collection, which lacks real-time and comprehensiveness. The present invention integrates smart wearable devices and a variety of health monitoring tools to collect multi-dimensional health data of the elderly in real time and continuously, covering physiological indicators such as heart rate, blood pressure, blood sugar, body temperature, sleep quality, etc., and combines information such as living habits and medical history to form a comprehensive health data set for the elderly. This comprehensive data collection not only improves the real-time and integrity of the data, but also can capture changes in the health status of the elderly in a timely manner and effectively prevent health risks. In addition, the system can remotely monitor the health status of the elderly, reduce the frequency of the elderly going to the hospital for examination, facilitate the daily life of the elderly, and reduce the burden on medical resources. This technology can greatly improve the efficiency of health data collection, making health management more scientific and accurate.

[0199] 2. Efficient data processing and analysis: Traditional health management systems are often difficult to process efficiently and conduct in-depth analysis when faced with large amounts of health data, resulting in untimely identification of health risks and untimely intervention measures. The present invention introduces the C4.5 decision tree algorithm to intelligently analyze and process the health data of the elderly. The decision tree algorithm can quickly build a classification model based on the characteristics of the data, and select the most discriminative features for data division by calculating the information gain ratio, thereby effectively improving the classification and prediction accuracy of health data. The system can not only process large-scale, multi-dimensional health data, but also model and analyze the data in an efficient manner, greatly improving the response speed of health management. Compared with traditional data processing methods, the decision tree algorithm has the advantages of strong interpretability and high execution efficiency. It can provide a clear decision path in the process of health data analysis, so that health managers can intuitively understand the health status of the elderly and take corresponding intervention measures in a timely manner.

[0200] 3. Generation of personalized health intervention plans: Another major innovation of the present invention is that it can generate highly personalized health intervention plans based on the specific health data of each elderly person. The system automatically recommends suitable dietary plans, exercise plans and medication reminders by analyzing the physiological indicators, living habits and medical history of the elderly, combined with the theory of traditional Chinese medicine. This personalized health intervention measure can more accurately meet the different health needs of the elderly and help them maintain a good state of health. Compared with the traditional "one-size-fits-all" health management, the present invention can achieve refined health management and provide tailored health guidance for each elderly person. This can not only effectively improve the quality of life of the elderly, but also reduce the risk of disease, delay the deterioration of health problems, and help the elderly better manage their health.

[0201] 4. Improve the accuracy and predictability of health risk identification: The present invention can identify the health risks of the elderly in advance and issue health warnings in a timely manner through in-depth mining and analysis of health data. The system combines historical health data with real-time monitoring data, and uses the predictive ability of the decision tree algorithm to accurately assess the health risk level of the elderly and make reasonable preventive suggestions for potential health problems. This predictive health management method can effectively prevent the deterioration of health problems, help the elderly take intervention measures as soon as possible, and avoid the occurrence of sudden illnesses. The risk warnings provided by the system can not only provide timely health protection for the elderly, but also provide references for the diagnosis and treatment of medical institutions, helping doctors to more accurately understand the health status of the elderly, thereby formulating more effective treatment plans. This risk identification mechanism plays an important role in improving the accuracy and efficiency of health management.

[0202] 5. Optimize the allocation of medical resources and reduce medical costs: In the traditional medical system, the allocation of medical resources often relies on manual judgment, which easily leads to waste or shortage of resources. The present invention can reasonably allocate medical resources and reduce unnecessary medical expenses through intelligent health data analysis and health risk assessment. For example, the system can predict the health risks of the elderly in advance, thereby avoiding unnecessary physical examinations or hospitalizations. At the same time, the personalized health intervention plans generated by the system can effectively reduce the occurrence of health problems and reduce the demand for medical resources. By optimizing the use of medical resources, the present invention not only improves the efficiency of health management, but also reduces unnecessary medical expenses for the elderly and reduces the operating costs of the medical system. This intelligent way of allocating medical resources can better balance medical needs and resource supply, and provide more economical and effective health management services for the elderly.

[0203] 6. Integrate Chinese medicine and diet to improve health management effects: The present invention combines modern medical technology with Chinese medicine theory to provide personalized dietary guidance and health management suggestions for the elderly. Chinese medicine and diet have a long history of application and relatively significant effects in improving the health status of the elderly. By combining Chinese medicine and diet with modern health management technology, the system can provide a more comprehensive health management plan for the elderly. Specifically, the system analyzes the physical characteristics of the elderly based on their health data, and automatically recommends a dietary combination plan suitable for their physical condition in combination with Chinese medicine theory. This personalized dietary recommendation can help the elderly regulate their bodies, promote health management and prevent diseases, and improve their overall health level. By combining Chinese medicine with big data technology, the present invention can provide scientific and comprehensive health management services for the elderly, further improving the effect of health management.

[0204] 7. Improve the quality of life and health level of the elderly: The ultimate goal of the present invention is to significantly improve the quality of life and health level of the elderly through intelligent health management methods. By real-time collection and analysis of the health data of the elderly, the system can promptly detect potential health problems and provide personalized health intervention suggestions. This proactive health management method can not only effectively delay the deterioration of health problems in the elderly, but also help them maintain a good living condition and prolong their healthy life span. In addition, the intelligent design of the system reduces the need for manual intervention and facilitates the elderly to manage their own health. This autonomy not only increases the enthusiasm of the elderly for health management, but also reduces dependence on medical institutions, helping the elderly to live more freely and comfortably. By improving the health level of the elderly, the present invention will provide better services for the health management of the elderly population and promote the improvement of the overall health level of society.

[0205] 8. Promote the development of intelligent health management technology: The present invention provides new ideas for the further development of intelligent health management technology by innovatively combining big data technology, decision tree algorithm and traditional Chinese medicine diet. With the continuous advancement of artificial intelligence technology, the field of health management is transforming from the traditional manual intervention mode to the direction of intelligence and automation. The successful application of the present invention not only verifies the application potential of big data technology and machine learning algorithms in health management, but also lays the foundation for the development of intelligent health management technology in the future. The intelligent health management system of the present invention has strong scalability and adaptability, and can be applied to a wider range of health management scenarios in the future. It is not only limited to the elderly group, but also can be extended to other special populations, such as patients with chronic diseases, pregnant women, children, etc. Through continuous optimization of algorithms and technologies, the present invention is expected to promote technological progress in the field of intelligent health management and provide society with more comprehensive and intelligent health management solutions.

[0206] Therefore, the present invention provides an innovative and practical solution for the health management of the elderly through comprehensive data collection, efficient data processing, personalized intervention plan generation, accurate health risk identification and optimized medical resource allocation. It can not only effectively improve the health level of the elderly, but also reduce medical costs and improve the utilization efficiency of medical resources. The widespread application of this system will greatly promote the development of intelligent health management technology and make positive contributions to the health protection and quality of life improvement of the elderly.

[0207] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0208] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. A health analysis system for the elderly based on the C4.5 decision tree algorithm, characterized in that: include: Data collection terminal, used to collect users' physiological health data in real time; A data processing module, used for cleaning, standardizing and storing the data collected by the data collection terminal; The algorithm analysis module uses the C4.5 decision tree algorithm to analyze the processed health data, build a health data classification model, calculate the information gain ratio of each health data feature based on information entropy and information gain ratio, and select the best feature for splitting based on the information gain ratio to predict the health risks of the elderly; wherein, according to the selected partitioning attribute, the data set is divided into several subsets, each subset corresponds to a child node; the C4.5 algorithm is recursively called for each child node, and the data set is continued to be divided according to the new partitioning attribute to generate a subtree of the child node; when the stop condition is met, the recursive process stops, and the current node becomes a leaf node, and the leaf node represents the predicted result of the health status; The personalized health intervention module generates personalized health intervention plans for users based on the health risk prediction results of the algorithm analysis module.

2. The system according to claim 1, characterized in that: The data acquisition terminal comprises: A smart wearable device with an embedded sensor for collecting the user's physiological index data in real time; the smart wearable device uploads the collected physiological index data to the cloud or local server for storage and processing via wireless communication; Lifestyle monitoring equipment, which is used to record the user's daily living habits data through questionnaire surveys or in combination with smart home devices. The lifestyle monitoring equipment will record the lifestyle data together with the physiological index data to form a comprehensive health data set; The medical history and medical record integration module is used to integrate the user's medical history and previous medical record data; the medical history and medical record data are obtained by connecting with the hospital's electronic medical record system, or collected by manual input, and stored together with real-time physiological data to build a complete health record of the user; The various devices in the data collection terminal transmit data through a standardized interface; at the same time, the health data of each device and each user are distinguished by a unique identifier.

3. The system according to claim 1, characterized in that Cleaning, standardizing and storing the data collected by the data collection terminal includes: Clean the original health data uploaded from the data collection terminal, automatically identify and eliminate abnormal data, redundant data and errors in the data collection process; Fill in missing physiological indicator data; use interpolation or inference algorithms based on historical data to fill in missing values; Standardize data from different devices and using different dimensions; convert data of different indicators to a unified scale through normalization processing methods; The processed data is securely stored in the cloud database and stored in time series to facilitate subsequent data analysis and query; at the same time, the data is backed up regularly and encryption technology is used to protect sensitive information and prevent data leakage.

4. The system according to claim 1, characterized in that: In the process of building a health data classification model, the information gain ratio is used to evaluate the importance of each feature, which includes the following steps: Calculate feature entropy: For each feature, calculate its entropy value on the data set. This entropy value is used to reflect the uncertainty or confusion of the data. Evaluate the information gain rate: Based on the calculated feature entropy value, further calculate the information gain rate of each feature. The information gain rate is an indicator to measure the effect of the feature on data classification. It indicates the uncertainty that can be reduced after using the feature for splitting; Select the best features: According to the information gain rate, select the features that can minimize the uncertainty of the data as the splitting features of the current node to ensure the accuracy of the decision tree model in classification or prediction; Recursive construction: For each subset, recursively perform feature selection and node splitting. The stopping conditions of recursive construction include: all features have been used up, the data purity of a node is high enough, that is, all samples belong to the same category, or the amount of data in the node is less than the preset threshold; Leaf node generation: When the stopping condition is reached, the current node is set as a leaf node and a specific prediction category is assigned to it; Construct a decision tree: Through the above steps, the optimal features are gradually selected for splitting, and a decision tree model is constructed as a health data classification model.

5. The system according to claim 4, characterized in that: For each feature, calculate its entropy value on the data set, including: For a data set S, its entropy H(S) is calculated as follows: H(S)=-∑(from i=1 to n)p_i*log(p_i) Among them, p_i represents the proportion of samples belonging to category i, and n is the total number of categories; the higher the entropy, the greater the uncertainty of the data set. Calculate the conditional entropy of features, including: For a certain feature A, the conditional entropy H(S|A) represents the data uncertainty under the given conditions of feature A: H(S|A)=∑(v∈Values(A))(|S_v| / |S|)*H(S_v) Among them, S_v represents the data subset whose feature A takes the value of v, |S_v| is the subset size, and |S| is the size of the original data set.

6. The system according to claim 5, characterized in that: Calculate the information gain rate of each feature, including: Calculate the reduced uncertainty after feature A selection, expressed as the following formula: Gain(S,A)=H(S)-H(S|A) Among them, the greater the information gain, the better the classification effect of feature A; Calculate the information gain ratio, expressed as the following formula: Gain_Ratio(S,A)=Gain(S,A) / H_A(S) Among them, H_A(S) is the intrinsic entropy of feature A, expressed as the following formula: H_A(S)=-∑(v∈Values(A))(|S_v| / |S|)*log 2 (|S_v| / |S|) Among them, the inherent entropy is used to measure the discrete degree of feature values, and the information gain ratio is used to overcome the problem that information gain is biased towards multi-valued features.

7. The system according to claim 1, characterized in that Through the health data classification model, the user's health risks are predicted, including: Data input and node judgment: The collected physiological health data, medical history and living habits information of the user are input into the root node of the decision tree model, wherein the root node is set based on the most important initial feature and is used to guide the data to the corresponding branch according to the feature of the input data; Recursive splitting and path selection: When data flows through each node in the tree, the decision tree model recursively judges each feature and selects the branch where the data should continue to flow according to the feature value until the data reaches the leaf node; Risk prediction of leaf nodes: When data reaches the leaf nodes of the decision tree, the final prediction result of the user's health risk is given based on all the characteristic conditions on the data flow path, where the leaf nodes represent specific health states; Warning and suggestions: If the prediction results indicate that the user is in a high-risk health state, a health assessment report will be automatically generated and targeted intervention suggestions will be provided.

8. The system according to claim 1, characterized in that: Pre-pruning and post-pruning techniques are also used to prevent the decision tree from overfitting, which includes the following steps: Application of pre-pruning technology: During the growth of the decision tree, stop conditions are set. When the decision tree reaches a preset depth, the number of samples in the node is lower than the threshold, or the information gain rate is lower than the predetermined standard, the tree is stopped from further splitting to control the complexity and size of the tree. Application of post-pruning technology: After the decision tree model is built, the model is verified using a test data set to evaluate the contribution of each node and branch to the model performance. Branches and leaves that do not significantly improve the generalization ability of the model or cause the model to overfit are pruned to simplify the model structure and improve the generalization ability of the model.

9. The system according to claim 8, characterized in that Also includes: The split stop evaluation module is used to evaluate whether the split can significantly improve the prediction ability of the model each time a node splits; The split stop evaluation module specifically performs the following steps: Determine whether the number of samples of the current node is less than the preset minimum sample number threshold. If so, stop splitting to avoid overfitting splitting when the sample size is very small; If the number of samples of the current node is not less than the preset minimum number of samples, the information gain after the split is further calculated, and it is determined whether the information gain is less than the preset information gain threshold. If so, it is considered that the contribution of the split to the model effect is insufficient, and the split is stopped; In the elderly health analysis system, when the samples contained in a certain node are sufficient to show that these elderly people have lower health risks, if further splitting cannot significantly improve the distinction of health status, that is, the information gain after splitting is less than the information gain threshold, then the split stop evaluation module stops the splitting here to avoid the model being too complicated.

10. The system according to claim 8, characterized in that Also includes: After the physiological health data is divided into a training set and a test set, the decision tree model is trained on the training set by iteratively adjusting the splitting conditions and pruning rules of the model, including: Based on the training results of the current training set, analyze the prediction accuracy of the model on the test set; According to the prediction accuracy on the test set, determine the splitting condition parameters and / or pruning rule parameters that need to be adjusted; Adjusting the splitting condition parameters and / or pruning rule parameters to form a new decision tree model; Retrain the training set using the adjusted new decision tree model and validate it again on the test set; Repeat the above steps until the prediction accuracy on the test set reaches the preset optimization standard or no longer improves significantly.

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