Cardiovascular disease onset risk prediction method and device incorporating environmental comprehensive factors

Abstract

The invention provides a cardiovascular disease onset risk prediction method and device incorporating environmental comprehensive factors, and relates to the technical field of computer data processing.The method comprises the steps that environmental comprehensive exposure and cardiovascular health related data are collected, and the data at least comprise environmental factors, crowd factors and risk factors; fitting analysis is carried out according to the environmental factors and the crowd factors to obtain comprehensive scores of the environmental factors; according to the comprehensive scores of the risk factors and the environmental factors, respectively constructing cardiovascular disease onset risk prediction models in different time periods for different types of investigators; and predicting the cardiovascular disease onset risk by using the cardiovascular disease onset risk prediction model to obtain a prediction result.

Description

Technical Field

[0001] The present invention relates to the technical field of computer data processing, and in particular to a method and device for predicting the risk of cardiovascular disease by incorporating comprehensive environmental factors. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] At present, a large number of studies have reported that harmful environmental factors such as air pollution and heavy metals have adverse effects on the cardiovascular system of the population. Cardiovascular disease is the main cause of disability and life loss. With the aging of my country's population and the increase in the level of cardiovascular risk factors, the mortality rate of cardiovascular disease has increased significantly, and its prevention and control situation has become increasingly severe. Primary prevention measures targeting cardiovascular disease risk factors can effectively delay and avoid the occurrence of cardiovascular disease, but there is currently a lack of technical methods for predicting the incidence of cardiovascular disease that take into account comprehensive environmental factors. The existing technology mainly has the following deficiencies:

[0004] First, the existing cardiovascular disease prediction technology methods do not take into account the environmental factors that have a greater impact on the incidence of cardiovascular disease, resulting in a lack of accuracy in model prediction;

[0005] Second, the public is actually exposed to a complex environment where multiple factors such as air, water and soil coexist. It is one-sided to focus only on exposure to a few factors in the past.

[0006] Due to the above-mentioned limitations of the technical methods for predicting the incidence of cardiovascular diseases, there is an urgent need to develop new technologies to overcome the problem that the prediction accuracy of existing research methods is still not high enough, and to support the prevention and control of cardiovascular diseases. Summary of the invention

[0007] In order to solve the problems existing in the prior art, the present invention proposes a method and device for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors. The present invention innovatively considers environmental factors in the cardiovascular disease incidence prediction model, and fits the comprehensive score of environmental factors according to the actual situation of comprehensive and complex exposure of environmental factors, and finally develops a cardiovascular disease risk prediction technology incorporating comprehensive environmental factors.

[0008] In a first aspect of an embodiment of the present invention, a method for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors is proposed, the method comprising:

[0009] Collect data related to comprehensive environmental exposure and cardiovascular health, which at least includes environmental factors, population factors and risk factors;

[0010] Fitting analysis was performed based on the environmental factors and population factors to obtain a comprehensive score of environmental factors;

[0011] According to the comprehensive scores of the risk factors and the environmental factors, for different types of survey subjects, respectively, a cardiovascular disease incidence risk prediction model in different time periods is constructed;

[0012] The cardiovascular disease risk prediction model is used to predict the risk of cardiovascular disease and obtain a prediction result.

[0013] In a second aspect of an embodiment of the present invention, a device for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors is provided, the device comprising:

[0014] A data collection module is used to collect comprehensive environmental exposure and cardiovascular health-related data, which at least includes environmental factors, population factors and risk factors;

[0015] A fitting analysis module, used to perform fitting analysis based on the environmental factors and population factors to obtain a comprehensive score of the environmental factors;

[0016] A model building module is used to build cardiovascular disease risk prediction models in different time periods for different types of survey subjects according to the comprehensive scores of the risk factors and the environmental factors;

[0017] The prediction module is used to predict the risk of cardiovascular disease by using the cardiovascular disease risk prediction model to obtain a prediction result.

[0018] In a third aspect of an embodiment of the present invention, a computer device is proposed, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a method for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors when executing the computer program.

[0019] In a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is proposed, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a method for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors is implemented.

[0020] In a fifth aspect of an embodiment of the present invention, a computer program product is proposed, which includes a computer program, and when the computer program is executed by a processor, a method for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors is implemented.

[0021] The cardiovascular disease risk prediction method and device proposed by the present invention incorporates comprehensive environmental factors by collecting comprehensive environmental exposure and cardiovascular health-related data, which at least include environmental factors, population factors and risk factors; fitting analysis is performed based on the environmental factors and population factors to obtain a comprehensive environmental factor score; based on the risk factors and the comprehensive environmental factor score, for different types of survey subjects, cardiovascular disease risk prediction models for different time periods are constructed respectively; using the cardiovascular disease risk prediction model, the cardiovascular disease risk is predicted to obtain a prediction result. The overall solution can improve the model prediction accuracy, comprehensively analyze the impact of complex environments on cardiovascular disease incidence, and provide strong technical support for cardiovascular disease prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 It is a schematic flow chart of a method for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors according to an embodiment of the present invention.

[0024] Figure 2 It is a flowchart of data collection according to an embodiment of the present invention.

[0025] Figure 3 It is a schematic diagram of a fitting analysis process according to an embodiment of the present invention.

[0026] Figure 4 It is a flowchart of model construction according to an embodiment of the present invention.

[0027] Figure 5 It is a schematic diagram of a risk prediction process according to an embodiment of the present invention.

[0028] Figure 6 It is a schematic diagram of the architecture of a device for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors according to an embodiment of the present invention.

[0029] Figure 7 It is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0031] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, device, apparatus, method or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0032] According to an embodiment of the present invention, a method and device for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors are proposed, which relate to the technical field of computer data processing.

[0033] The principle and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

[0034] Figure 1 FIG. 1 is a flow chart of a method for predicting cardiovascular disease risk by incorporating comprehensive environmental factors according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0035] S101, collect data related to comprehensive environmental exposure and cardiovascular health, which at least includes environmental factors, population factors and risk factors;

[0036] S102, performing fitting analysis based on the environmental factors and population factors to obtain a comprehensive score of the environmental factors;

[0037] S103, constructing cardiovascular disease risk prediction models in different time periods for different types of survey subjects according to the comprehensive scores of the risk factors and the environmental factors;

[0038] S104, predicting the risk of cardiovascular disease using the cardiovascular disease risk prediction model to obtain a prediction result.

[0039] In order to explain more clearly the above-mentioned method for predicting the risk of cardiovascular disease that incorporates comprehensive environmental factors, each step will be explained in detail below.

[0040] In one embodiment, for S101, comprehensive environmental exposure and cardiovascular health related data are collected, such as Figure 2 As shown, the specific process includes:

[0041] S101-1, determine the target population, which at least includes people without cardiovascular disease;

[0042] S101-2, collect baseline environmental pollutant exposure information of the target population in the air, water, and soil, including at least gaseous pollutants, particulate matter, heavy metals, and organic compounds;

[0043] S101-3, collect the basic sociodemographic information of the target population at baseline, including age, education level, income level, occupation, and place of residence (urban or rural, north or south); basic variables for predicting the incidence of cardiovascular disease, including BMI, blood pressure, heart rate, waist circumference, smoking history, family history of cardiovascular disease, history of atrial fibrillation, history of diabetes, blood sugar (fasting blood sugar, glycosylated hemoglobin), blood lipids (TC, HDL-C, LDL-C, TG), medication history (antihypertensive drugs, hypoglycemic drugs, lipid-lowering drugs, anticoagulant / antiplatelet drugs); and the incidence of cardiovascular disease in each person after 5 or 10 years of follow-up, including the time of diagnosis and type of disease.

[0044] In one embodiment, for S102, a fitting analysis is performed based on the environmental factors and population factors to obtain a comprehensive score of the environmental factors, such as Figure 3 As shown, the specific process includes:

[0045] S102-1, construct a machine learning random forest model, take the environmental factors and population factors as covariates, and input whether cardiovascular disease occurs as the dependent variable into the model, obtain feature importance parameters through the machine learning random forest model, and output the top 30 important features, select environmental factors from the important features as the screened environmental variables.

[0046] S102-2, construct a logistic regression model, use the screened environmental variables as covariates, and whether cardiovascular disease occurs as the dependent variable to enter the model. After fitting the model, extract the model coefficients of all environmental variables;

[0047] S102-3, using the model coefficients, fit the Logistic regression model again, substitute the actual environmental factor exposure value of each survey subject in the target population, and calculate the comprehensive environmental factor score for each survey subject.

[0048] In one embodiment, for S103, according to the comprehensive scores of the risk factors and the environmental factors, for different types of survey subjects, cardiovascular disease risk prediction models for different time periods are constructed respectively, such as Figure 4 As shown, the specific methods include:

[0049] S103-1, based on the risk factors and the comprehensive scores of the environmental factors, construct a 5-year / 10-year COX proportional hazard model for the incidence of cardiovascular disease in male and female survey subjects respectively; wherein the model includes a comprehensive score variable of environmental factors, a variable with a comprehensive discriminant improvement index>6%, and a statistically significant interaction term;

[0050] S103-2, COX proportional hazard model for cardiovascular disease, was used to assess the risk of each respondent.

[0051] Specifically, the calculation formula is as follows:

[0052] f[x,M]=β1(x1-M1)+...+β p (x p -M p );

[0053] Among them, f[x,M] is the probability of disease occurrence for each survey subject; β1 to β p is the partial regression coefficient of each risk factor; x1 to x p The level of each risk factor for each individual, including the comprehensive score of environmental factors; M1 to M p is the average level of each risk factor in the whole population;

[0054] P0=1-S0(t) expf[x,M] ;

[0055] Among them, P0 is the risk probability of cardiovascular disease; S0(t) is the baseline survival function within time t, which is the survival function assuming that the levels of each risk factor are at the average level.

[0056] In one embodiment, for S104, the cardiovascular disease risk prediction model is used to predict the risk of cardiovascular disease to obtain a prediction result, such as Figure 5 As shown, the specific methods include:

[0057] S104-1, the prediction ability of the model is evaluated by calibration and discrimination; the population is divided into multiple groups according to the predicted risk of the model, the average predicted risk and the actual observed risk of each group are calculated, and a calibration graph is drawn. The Hosmer-Lemeshow chi-square test is used to calculate the statistical value. If the statistical value is <20 or the probability p>0.05, the model calibration is determined to be up to standard. The discrimination is evaluated by using the C statistic. If the statistic C is ≥0.70, the model's discrimination ability is determined to be up to standard.

[0058] S104-2, using a cardiovascular disease risk prediction model that meets the calibration standard and the discrimination ability standard, predict the risk of cardiovascular disease and obtain a prediction result.

[0059] The model's ability to predict cardiovascular disease incidence is superior to previous cardiovascular disease models that did not consider environmental factors.

[0060] It should be noted that, although the operations of the method of the present invention are described in a specific order in the above embodiments and the accompanying drawings, this does not require or imply that the operations must be performed in the specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0061] After introducing the method of the exemplary embodiment of the present invention, next, refer to Figure 6 A device for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors according to an exemplary embodiment of the present invention is introduced.

[0062] The implementation of the cardiovascular disease risk prediction device incorporating comprehensive environmental factors can refer to the implementation of the above method, and the repetitions will not be repeated. The terms "module" or "unit" used below can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0063] Based on the same inventive concept, the present invention also proposes a cardiovascular disease risk prediction device that incorporates comprehensive environmental factors, such as Figure 6 As shown, the device comprises:

[0064] The data collection module 610 is used to collect comprehensive environmental exposure and cardiovascular health related data, which at least includes environmental factors, population factors and risk factors;

[0065] A fitting analysis module 620 is used to perform a fitting analysis based on the environmental factors and population factors to obtain a comprehensive score of the environmental factors;

[0066] The model building module 630 is used to build cardiovascular disease risk prediction models in different time periods for different types of survey subjects according to the comprehensive scores of the risk factors and the environmental factors;

[0067] The prediction module 640 is used to predict the risk of cardiovascular disease by using the cardiovascular disease risk prediction model to obtain a prediction result.

[0068] In one embodiment, the data collection module 610 collects comprehensive environmental exposure and cardiovascular health related data, including:

[0069] Identify the target population, which at least includes people without cardiovascular disease;

[0070] Collect baseline information on the target population’s exposure to environmental pollutants in the air, water, and soil, including at least gaseous pollutants, particulate matter, heavy metals, and organic compounds;

[0071] Collect basic sociodemographic information of the target population at baseline, including age, education level, income level, occupation, and place of residence; basic variables for predicting the incidence of cardiovascular disease, including BMI, blood pressure, heart rate, waist circumference, smoking history, family history of cardiovascular disease, history of atrial fibrillation, history of diabetes, blood sugar, blood lipids, and medication history; and the incidence of cardiovascular disease in each person after 5 or 10 years of follow-up, including the time of diagnosis and type of disease.

[0072] In one embodiment, the fitting analysis module 620 performs fitting analysis based on the environmental factors and population factors to obtain a comprehensive score of environmental factors, including:

[0073] A machine learning random forest model was constructed, and the environmental factors and population factors were used as covariates, and whether cardiovascular disease occurred was input into the model as the dependent variable. Feature importance parameters were obtained through the machine learning random forest model, and the top 30 important features were output. Environmental factors were selected from the important features as the screened environmental variables.

[0074] In one embodiment, the fitting analysis module 620 performs fitting analysis based on the environmental factors and population factors to obtain a comprehensive score of environmental factors, including:

[0075] A logistic regression model was constructed, and the screened environmental variables were used as covariates, and the incidence of cardiovascular disease was input into the model as the dependent variable. After fitting the model, the model coefficients of all environmental variables were extracted.

[0076] The model coefficients were used to fit the Logistic regression model again, and the actual environmental factor exposure values ​​of each respondent in the target population were substituted to obtain the comprehensive environmental factor score for each respondent.

[0077] In one embodiment, the model building module 630 builds cardiovascular disease risk prediction models for different time periods for different types of survey subjects according to the comprehensive scores of the risk factors and the environmental factors, including:

[0078] According to the risk factors and the comprehensive scores of the environmental factors, a 5-year / 10-year COX proportional hazard model for cardiovascular disease incidence was constructed in male and female survey subjects respectively; wherein the model includes the comprehensive score variable of environmental factors, the variable with comprehensive discrimination improvement index>6%, and the interaction term with statistical significance;

[0079] The COX proportional hazard model for cardiovascular disease was used to assess the risk of each subject, using the following calculation formula:

[0080] f[x,M]=β1(x1-M1)+...+β p (x p -M p );

[0081] Among them, f[x,M] is the probability of disease occurrence for each survey subject; β1 to β p is the partial regression coefficient of each risk factor; x1 to x p The level of each risk factor for each individual, including the comprehensive score of environmental factors; M1 to M p is the average level of each risk factor in the whole population;

[0082] P0=1-S0(t) expf[x,M] ;

[0083] Among them, P0 is the risk probability of cardiovascular disease; S0(t) is the baseline survival function within time t, which is the survival function assuming that the levels of each risk factor are at the average level.

[0084] In one embodiment, the prediction module 640 uses the cardiovascular disease risk prediction model to predict the risk of cardiovascular disease and obtains a prediction result, including:

[0085] The prediction ability of the model was evaluated by calibration and discrimination. The population was divided into multiple groups according to the predicted risk of the model, and the average predicted risk and actual observed risk of each group were calculated and a calibration graph was drawn. The Hosmer-Lemeshow chi-square test was used to calculate the statistical value. If the statistical value was <20 or the probability p>0.05, the model calibration was determined to be up to standard. The discrimination was evaluated by using the C statistic. If the statistic C was ≥0.70, the model's discrimination ability was determined to be up to standard.

[0086] Using a cardiovascular disease risk prediction model that meets the calibration and discrimination standards, the risk of cardiovascular disease is predicted to obtain prediction results.

[0087] It should be noted that, although several modules of the cardiovascular disease risk prediction device incorporating comprehensive environmental factors are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules described above can be concretized in one module. Conversely, the features and functions of one module described above can be further divided into multiple modules to be concretized.

[0088] Based on the above invention concept, Figure 7As shown, the present invention also proposes a computer device 700, including a memory 710, a processor 720, and a computer program 730 stored in the memory 710 and executable on the processor 720, wherein the processor 720 implements the aforementioned cardiovascular disease risk prediction method incorporating comprehensive environmental factors when executing the computer program 730.

[0089] Based on the aforementioned inventive concept, the present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the aforementioned method for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors.

[0090] Based on the aforementioned inventive concept, the present invention proposes a computer program product, which includes a computer program, and when the computer program is executed by a processor, a method for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors is implemented.

[0091] The cardiovascular disease risk prediction method and device proposed by the present invention incorporates comprehensive environmental factors by collecting comprehensive environmental exposure and cardiovascular health-related data, which at least include environmental factors, population factors and risk factors; fitting analysis is performed based on the environmental factors and population factors to obtain a comprehensive environmental factor score; based on the risk factors and the comprehensive environmental factor score, for different types of survey subjects, cardiovascular disease risk prediction models for different time periods are constructed respectively; using the cardiovascular disease risk prediction model, the cardiovascular disease risk is predicted to obtain a prediction result. The overall solution can improve the model prediction accuracy, comprehensively analyze the impact of complex environments on cardiovascular disease incidence, and provide strong technical support for cardiovascular disease prevention and control.

[0092] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of laws and regulations.

[0093] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] The present invention is described with reference to flowcharts and / or block diagrams of methods and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for predicting the risk of cardiovascular disease by incorporating comprehensive environmental factors, characterized in that: The method includes: Collect data related to comprehensive environmental exposure and cardiovascular health, which at least includes environmental factors, population factors and risk factors; Fitting analysis was performed based on the environmental factors and population factors to obtain a comprehensive score of environmental factors; According to the comprehensive scores of the risk factors and the environmental factors, for different types of survey subjects, respectively, a cardiovascular disease incidence risk prediction model in different time periods is constructed; The cardiovascular disease risk prediction model is used to predict the risk of cardiovascular disease and obtain a prediction result.

2. The method for predicting cardiovascular disease risk by incorporating comprehensive environmental factors according to claim 1, characterized in that: Collect comprehensive environmental exposure and cardiovascular health data, including: Identify the target population, which at least includes people without cardiovascular disease; Collect baseline information on the target population’s exposure to environmental pollutants in the air, water, and soil, including at least gaseous pollutants, particulate matter, heavy metals, and organic compounds; Collect basic sociodemographic information of the target population at baseline, including age, education level, income level, occupation, and place of residence; basic variables for predicting the incidence of cardiovascular disease, including BMI, blood pressure, heart rate, waist circumference, smoking history, family history of cardiovascular disease, history of atrial fibrillation, history of diabetes, blood sugar, blood lipids, and medication history; and the incidence of cardiovascular disease in each person after 5 or 10 years of follow-up, including the time of diagnosis and type of disease.

3. The method for predicting cardiovascular disease risk by incorporating comprehensive environmental factors according to claim 1, characterized in that: A fitting analysis was performed based on the environmental factors and population factors to obtain a comprehensive score of environmental factors, including: A machine learning random forest model was constructed, and the environmental factors and population factors were used as covariates, and whether cardiovascular disease occurred was input into the model as the dependent variable. Feature importance parameters were obtained through the machine learning random forest model, and the top 30 important features were output. Environmental factors were selected from the important features as the screened environmental variables.

4. The method for predicting cardiovascular disease risk by incorporating comprehensive environmental factors according to claim 3, characterized in that: A fitting analysis was performed based on the environmental factors and population factors to obtain a comprehensive score of environmental factors, including: A logistic regression model was constructed, and the screened environmental variables were used as covariates, and the incidence of cardiovascular disease was input into the model as the dependent variable. After fitting the model, the model coefficients of all environmental variables were extracted. The model coefficients were used to fit the Logistic regression model again, and the actual environmental factor exposure values ​​of each respondent in the target population were substituted to obtain the comprehensive environmental factor score for each respondent.

5. The method for predicting cardiovascular disease risk by incorporating comprehensive environmental factors according to claim 1, characterized in that: According to the comprehensive scores of the risk factors and the environmental factors, for different types of survey subjects, cardiovascular disease risk prediction models in different time periods are constructed, including: According to the risk factors and the comprehensive scores of the environmental factors, a 5-year / 10-year COX proportional hazard model for cardiovascular disease incidence was constructed in male and female survey subjects respectively; wherein the model includes the comprehensive score variable of environmental factors, the variable with comprehensive discrimination improvement index>6%, and the interaction term with statistical significance; The COX proportional hazard model for cardiovascular disease was used to assess the risk of each subject, using the following calculation formula: f[x,M]=β1(x1-M1)+...+β p (x p -M p ); Among them, f[x,M] is the probability of disease occurrence for each survey subject; β1 to β p is the partial regression coefficient of each risk factor; x1 to x p The level of each risk factor for each individual, including the comprehensive score of environmental factors; M1 to M p is the average level of each risk factor in the whole population; P0=1-S0(t) expf[x,M] ; Among them, P0 is the risk probability of cardiovascular disease; S0(t) is the baseline survival function within time t, which is the survival function assuming that the levels of each risk factor are at the average level.

6. The method for predicting cardiovascular disease risk by incorporating comprehensive environmental factors according to claim 1, characterized in that: The cardiovascular disease risk prediction model is used to predict the risk of cardiovascular disease and obtain prediction results, including: The prediction ability of the model was evaluated by calibration and discrimination. The population was divided into multiple groups according to the predicted risk of the model, and the average predicted risk and actual observed risk of each group were calculated and a calibration graph was drawn. The Hosmer-Lemeshow chi-square test was used to calculate the statistical value. If the statistical value was <20 or the probability p>0.05, the model calibration was determined to be up to standard. The discrimination was evaluated by using the C statistic. If the statistic C was ≥0.70, the model's discrimination ability was determined to be up to standard. Using a cardiovascular disease risk prediction model that meets the calibration and discrimination standards, the risk of cardiovascular disease is predicted to obtain prediction results.

7. A device for predicting the risk of cardiovascular disease incorporating comprehensive environmental factors, characterized in that: The device includes: A data collection module is used to collect comprehensive environmental exposure and cardiovascular health-related data, which at least includes environmental factors, population factors and risk factors; A fitting analysis module, used to perform fitting analysis based on the environmental factors and population factors to obtain a comprehensive score of the environmental factors; A model building module is used to build cardiovascular disease risk prediction models in different time periods for different types of survey subjects according to the comprehensive scores of the risk factors and the environmental factors; The prediction module is used to predict the risk of cardiovascular disease by using the cardiovascular disease risk prediction model to obtain a prediction result.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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