Chronic disease health risk assessment method and system

Abstract

The invention discloses a chronic disease health risk assessment method and system, and belongs to the technical field of risk assessment, and the method specifically comprises the steps: collecting the health data of a target individual or group, carrying out the preprocessing of the collected health data, recognizing the risk factors of the occurrence of a chronic disease through an epidemiological algorithm based on the preprocessed health data, and carrying out the recognition of the risk factors. Establishing a chronic disease health risk assessment model by using a statistical strategy according to the identified risk factors, applying the established risk assessment model to a target individual or group, and calculating the risk probability and grade of chronic disease according to the calculation result of the risk assessment model. The chronic disease health risk assessment model is verified and calibrated by using cross validation and sensitivity analysis, individual chronic disease risk levels, health suggestions and management measures are interpreted and reported, a personalized health management plan is made, and a personalized, scientific, accurate, comprehensive and comprehensive assessment system is provided.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk assessment, and specifically relates to a method and system for assessing health risks of chronic diseases. Background Art

[0002] With the change of lifestyle and the intensification of the aging population, chronic diseases have become a major health problem worldwide. These diseases not only affect the quality of life of patients, but also bring huge economic burden to society. Therefore, early prevention and intervention of chronic diseases are crucial. Risk assessment technology is one of the important means to prevent and control chronic diseases. Through comprehensive analysis of individual lifestyles, genetic factors, and environmental factors, the risk of chronic diseases can be assessed, so that preventive measures can be taken. At the same time, with the development of medical information and data, a large amount of health data is collected and analyzed. These data include patients' clinical information, physical examination results, and lifestyle information, which provide a rich source of data for chronic disease health risk assessment. By mining and analyzing these data, risk factors related to the occurrence of chronic diseases can be discovered, providing a scientific basis for risk assessment.

[0003] For example, a Chinese patent with publication number CN110189829A discloses a method and system for assessing the risk of major chronic diseases in power occupational groups, including: obtaining sampled data of power occupational groups, the data categories corresponding to the data of power occupational groups include: personal basic information data, personal living habits data, mental health status data, occupation-related factor data, physical examination data, and human resources data; obtaining physical indicators that need to be analyzed and the risk levels contained therein; establishing a logistic regression model based on the data categories, physical indicators and the risk levels contained therein corresponding to the data of power occupational groups; entering the data of power occupational groups into the established logistic regression model, solving the logistic regression model with the entered data of power occupational groups, and obtaining the probability corresponding to each risk level of the physical indicators.

[0004] For example, a Chinese patent with authorization announcement number CN112732690B discloses a stable system and method for chronic disease detection and risk assessment, which obtains multi-dimensional health information of the detected user, extracts and constructs features of the multi-dimensional health information, and obtains standardized data; performs feature reconstruction and feature weight calculation based on sample data in the database and the standardized data to determine the reconstructed input feature data; predicts the health of the detected user based on the reconstructed input feature data to obtain a prediction result, and feeds the prediction result and the obtained user data back to the database; performs noise identification on the user data and the prediction result to determine the noise data, and updates all data in the database based on the noise data, thereby solving the error data caused by subjective factors in the original medical data sample library, and iterating the data in the sample library to make the sample core point gradually stable.

[0005] The above existing technologies all have the following problems: 1) The logistic regression model usually assumes that the data are independent of each other and obey a certain distribution. If this assumption is not true, the prediction results of the model may be biased and perform poorly on new data; 2) The accuracy is highly dependent on the quality of the acquired multi-dimensional health information, such as CN112732690B, and involves complex calculations and data processing, resulting in a long system response time, which is not conducive to real-time evaluation. At the same time, the method is based on historical data for prediction and evaluation, and lacks dynamics. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes a chronic disease health risk assessment method and system, which collects health data of target individuals or groups, pre-processes the collected health data, identifies risk factors for chronic diseases through epidemiological algorithms based on the pre-processed health data, establishes a chronic disease health risk assessment model based on the identified risk factors using statistical strategies, applies the established risk assessment model to the target individuals or groups, calculates the risk probability and level of chronic diseases based on the calculation results of the risk assessment model, verifies and calibrates the chronic disease health risk assessment model using cross-validation and sensitivity analysis, interprets and reports individual chronic disease risk levels, health recommendations and management measures, and formulates personalized health management plans.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A chronic disease health risk assessment method, comprising:

[0009] Step S1: Determine the goal, scope and type of chronic diseases to be assessed for health risk assessment, collect health data of target individuals or groups from medical records, follow-up medical scales, health examinations, and questionnaires, and pre-process the collected health data;

[0010] Step S2: Based on the preprocessed health data, the risk factors for chronic diseases are identified through epidemiological algorithms, and a chronic disease health risk assessment model is established using statistical strategies based on the identified risk factors;

[0011] Step S3: Apply the established chronic disease health risk assessment model to the target individual or group, calculate the risk probability and level of chronic disease according to the calculation results of the chronic disease health risk assessment model, and use cross-validation and sensitivity analysis to verify and calibrate the chronic disease health risk assessment model;

[0012] Step S4: Interpret and report the individual's chronic disease risk level, health recommendations and management measures, develop a personalized health management plan, and regularly update and monitor the individual's health data to complete early prevention and intervention of chronic diseases.

[0013] Specifically, the specific steps of preprocessing the collected health data in step S1 include:

[0014] S101: Use programming languages ​​to remove duplicate, erroneous or incomplete data and convert continuous variables into categorical variables by equal interval grouping;

[0015] S102: Encode categorical variables, where male is represented by 1 and female is represented by 0;

[0016] S103: Standardize the encoded data.

[0017] Specifically, the specific steps of step S2 include:

[0018] S201: Set the pre-processed health data to X = {x1, x2, ..., x i}, using epidemiological algorithms to analyze X={x1,x2,...,x i}, identify the risk factors associated with the occurrence of chronic diseases Y = {y j},j=1,...,4, determine each risk factor y j The weight w j , where x i represents the type of health data after preprocessing, i represents the number of types of health data after preprocessing and 1≤i≤5, j represents the number of types of risk factors, y j Represents the type of risk factor. The specific formula is:

[0019]

[0020] Where h(t,Y) represents the risk factor Y={y j}, j = 1, ..., 4, h0(t) represents the basic risk function, λ j represents the coefficient estimated by regression analysis, exp(·) represents the exponential function, m represents the mth risk factor, and ln(·) represents the logarithmic function;

[0021] S202: Based on the identified risk factors and the weights of each risk factor, a chronic disease health risk assessment model is established using statistical strategies to determine the parameters and assessment criteria of the chronic disease health risk assessment model.

[0022] Specifically, the specific steps of step S3 include:

[0023] S301: The pre-processed health data X = {x1, x2, ..., x i}The data is divided into k subsets through data segmentation, and k-1 subsets are used as training sets, and the remaining subset is used as a validation set to train the chronic disease health risk assessment model;

[0024] S302: Calculate the risk probability or level of chronic diseases based on the calculation results of the chronic disease health risk assessment model. The formula is:

[0025]

[0026] Among them, P(X=m+1|Y) represents the probability of chronic disease occurrence under given risk factor Y;

[0027] S303: Verify the trained chronic disease health risk assessment model and calculate the performance index Q = {Acc, S, Sc, Pr, F1, AUC} of the chronic disease health risk assessment model, where Acc represents accuracy, S represents sensitivity, Sc represents specificity, Pr represents precision, F1 represents F1 score, and AUC represents the area under the receiver operating characteristic curve;

[0028] S304: Repeat S302-S303 until each subset is used as a validation set once, and obtain the average value of the performance indicator for each validation. Get the overall performance evaluation of the chronic disease health risk assessment model;

[0029] S305: If the overall performance evaluation result of the chronic disease health risk assessment model meets the requirements, the weight w of each risk factor is j Adjust according to percentage a to get the adjusted risk factor weight w′ j And performance index Q′, calculate the performance index changes of the chronic disease health risk assessment model under different risk factor values, and evaluate the sensitivity of the chronic disease health risk assessment model to each risk factor. The formula is:

[0030]

[0031] Among them, Y′ represents the adjusted risk factor value, P′(X=m+1|Y′) represents the probability of chronic disease occurrence under the adjusted risk factor Y′, Indicates the change value of performance index;

[0032] S306: Based on the results of sensitivity analysis, determine the impact of each risk factor on the prediction results of the chronic disease health risk assessment model. If the adjustment of the risk factor leads to a change in the performance indicator value If the value is larger, it is considered that the chronic disease health risk assessment model is more sensitive to this risk factor.

[0033] Specifically, the risk level of chronic diseases in step S3 includes three levels: high risk, medium risk, and low risk, and the classification criteria are:

[0034]

[0035] Among them, low, middle, and high represent low risk, medium risk, and high risk, respectively.

[0036] Specifically, the risk factors in step S2 include lifestyle, environmental factors, genetic factors and psychological state.

[0037] Specifically, the health management plan in step S4 includes lifestyle adjustment, regular monitoring, and preventive intervention.

[0038] A chronic disease health risk assessment system, comprising: a questionnaire module, a risk calculation module, an assessment report module, a health management module, and a data update and monitoring module;

[0039] The questionnaire module is used to collect chronic disease health risk data;

[0040] The risk calculation module is used to calculate the risk of the person being assessed developing a chronic disease within a certain period of time in the future;

[0041] The assessment report module is used to present the risk assessment results to the user in the form of a report;

[0042] The health management module is used to provide users with personalized health management plans;

[0043] The data updating and monitoring module is used to regularly update and monitor individual health data and evaluate the performance of the model.

[0044] Specifically, the risk calculation module includes: a data processing unit, a risk assessment algorithm unit;

[0045] The data processing unit is responsible for cleaning, converting and standardizing the collected data;

[0046] The risk assessment algorithm unit is used to calculate and assess the risk of an individual or a group suffering from a chronic disease.

[0047] Specifically, the evaluation report module includes: a report generation unit and a report interpretation unit;

[0048] The report generating unit is used to generate an evaluation report according to the calculation results;

[0049] The report interpretation unit is used to interpret and explain the calculated risk assessment results, analyze the factors affecting health risks, and provide personalized health guidance.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. The present invention proposes a chronic disease health risk assessment method, and optimizes and improves the architecture, operating steps and processes. The system has the advantages of simple process, low investment and operating costs, and low production work cost.

[0052] 2. The present invention proposes a chronic disease health risk assessment method. By collecting and analyzing the health data of target individuals or groups, it can conduct personalized chronic disease risk assessments based on each person's specific situation, help identify each person's unique risk factors, and thus formulate a health management plan that better meets individual needs. At the same time, by assessing the risk probability and level of chronic diseases, it can provide early warnings in the early stages of the development of chronic diseases, and take corresponding preventive or intervention measures, which helps reduce the probability of chronic diseases and mitigate their harm to human health.

[0053] 3. The present invention proposes a method for assessing the health risk of chronic diseases. It uses epidemiological algorithms and statistical strategies to establish an assessment model, and verifies and calibrates it through methods such as cross-validation and sensitivity analysis, thereby improving the accuracy of the assessment. By interpreting and reporting an individual's chronic disease risk level, health recommendations and management measures, it helps to improve the individual's attention to and management awareness of their own health status. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of a chronic disease health risk assessment method of the present invention;

[0055] Figure 2 This is an overall flow chart of a chronic disease health risk assessment method of the present invention;

[0056] Figure 3 This is an architecture diagram of a chronic disease health risk assessment system of the present invention. DETAILED DESCRIPTION

[0057] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, in the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate the orientation or position relationship based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "No. 1", "No. 2" and "No. 3" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. The present invention is further explained below in conjunction with specific embodiments.

[0058] Example 1

[0059] See also Figure 1-Figure 2 , an embodiment provided by the present invention: a method for assessing health risk of chronic diseases, comprising the following steps:

[0060] Step S1: Determine the goal, scope and type of chronic diseases to be assessed for health risk assessment, collect health data of target individuals or groups from medical records, follow-up medical scales, health examinations, and questionnaires, and pre-process the collected health data;

[0061] The health data in step S1 includes physiological and biochemical indicators, lifestyle data, personal or family health history, medical examination results, and questionnaire survey results.

[0062] Among them, physiological and biochemical indicators: such as blood pressure, blood sugar, blood lipids, heart rate, and blood and urine biochemical test results; lifestyle data: including information on living habits such as diet, exercise, smoking, and drinking; personal or family health history: such as personal medical history, family genetic disease history; medical examination results: such as electrocardiogram, ultrasound, X-ray, CT and other medical imaging results; questionnaire survey results: including questionnaire information on self-assessed health status, quality of life, and mental health.

[0063] Furthermore, chronic disease types include cardiovascular and cerebrovascular diseases, metabolic diseases, tumors, respiratory diseases, and mental illnesses. Among them, cardiovascular and cerebrovascular diseases include coronary heart disease, cerebral thrombosis, sequelae of cerebral hemorrhage, rheumatic heart disease, hypertension, and hypertensive heart disease. Metabolic diseases include diabetes, osteoporosis, hyperlipidemia, and hyperuricemia. Tumors include benign and malignant tumors. Respiratory diseases include chronic bronchitis, emphysema, bronchial asthma, and chronic obstructive pulmonary disease. Mental illnesses include psychosis, depression, anxiety, and neurosis.

[0064] Other conditions that are classified as chronic diseases include:

[0065] Digestive system diseases: such as chronic gastritis, gastric ulcer, hepatitis, pancreatitis; urinary and reproductive system diseases: such as prostatitis, prostatic hyperplasia, chronic nephritis, chronic epididymitis; blood system diseases: such as hemophilia, anemia; nervous system diseases: such as Parkinson's disease, epilepsy, multiple sclerosis; immune system diseases: such as rheumatoid arthritis, systemic sclerosis; others: such as chronic pain, chronic fatigue syndrome.

[0066] The assessment scope refers to age, gender, region, and occupation.

[0067] Step S2: Based on the preprocessed health data, the risk factors for chronic diseases are identified through epidemiological algorithms, and a chronic disease health risk assessment model is established using statistical strategies based on the identified risk factors;

[0068] The principles of epidemiology are an important theoretical basis for chronic disease health risk assessment. They mainly study the occurrence, development, distribution patterns and influencing factors of diseases in the population. The core of the principles of epidemiology lies in measuring the burden of the studied diseases in the population through quantitative research methods, analyzing the relationship between exposure and disease, and exploring the causes of disease occurrence and spread, so as to achieve the purpose of preventing and controlling the spread of diseases.

[0069] In the assessment of chronic disease health risks, the basic algorithms of epidemiological principles mainly include the following aspects:

[0070] 1) Descriptive epidemiology algorithm: Descriptive epidemiology is the basis of epidemiology. It mainly uses statistical methods to describe the distribution characteristics of diseases, including the frequency, incidence, mortality, and epidemic trends of diseases, in order to understand the basic situation of the disease.

[0071] 2) Analytical epidemiology algorithm: Analytical epidemiology mainly studies the relationship between diseases and various factors, and uses statistical analysis methods to find out the risk factors and protective factors of diseases, in order to deeply understand the occurrence mechanism of diseases;

[0072] 3) Experimental epidemiology algorithm: Experimental epidemiology evaluates the impact of intervention measures on the occurrence and development of diseases by designing and implementing intervention trials to evaluate the effectiveness of prevention and control measures.

[0073] Step S3: Apply the established chronic disease health risk assessment model to the target individual or group, calculate the risk probability and level of chronic disease according to the calculation results of the chronic disease health risk assessment model, and use cross-validation and sensitivity analysis to verify and calibrate the chronic disease health risk assessment model;

[0074] Step S4: Interpret and report the individual's chronic disease risk level, health recommendations and management measures, develop a personalized health management plan, and regularly update and monitor the individual's health data to complete early prevention and intervention of chronic diseases.

[0075] Provide targeted health advice and management measures including dietary adjustments, increased physical exercise, improved lifestyle habits, and regular physical examinations.

[0076] The specific steps of preprocessing the collected health data in step S1 include:

[0077] S101: Use programming languages ​​to remove duplicate, erroneous or incomplete data and convert continuous variables into categorical variables by equal interval grouping;

[0078] S102: Encode categorical variables, where male is represented by 1 and female is represented by 0;

[0079] S103: Standardize the encoded data.

[0080] The preprocessed data refers to the health data that has undergone a series of cleaning, conversion and standardization processes. The purpose of the preprocessing in the present invention is to improve the quality and usability of the data.

[0081] The specific steps of step S2 include:

[0082] S201: Set the pre-processed health data to X = {x1, x2, ..., x i}, using epidemiological algorithms to analyze X={x1,x2,...,x i}, identify the risk factors associated with the occurrence of chronic diseases Y = {y j},j=1,...,4, determine each risk factor y j The weight w j , where x i represents the type of health data after preprocessing, i represents the number of types of health data after preprocessing and 1≤i≤5, j represents the number of types of risk factors, y j Represents the type of risk factor. The specific formula is:

[0083]

[0084] Where h(t,Y) represents the risk factor Y={y j}, j = 1, ..., 4, h0(t) represents the basic risk function, λ j represents the coefficient estimated by regression analysis, exp(·) represents the exponential function, m represents the mth risk factor, and w j is the effect of each risk factor on the risk of chronic diseases, ln(·) represents the logarithmic function;

[0085] Use epidemiological algorithms to analyze preprocessed health data X = {x1, x2, ..., xi}, specific steps to identify risk factors associated with the development of chronic diseases include:

[0086] (1) The preprocessed health data X = {x1, x2, ..., x i}Presented in tabular form to ensure that the data format is suitable for epidemiological analysis;

[0087] (2) Select the epidemiological analysis method of multivariate logistic regression based on the research purpose and data type, and define and classify possible risk factors based on existing research and knowledge, and encode these factors for input into the analysis model;

[0088] (3) Preliminary analysis of the relationship between each risk factor and the occurrence of chronic diseases, using the t-test method to determine the strength of the association between each risk factor and the disease;

[0089] (4) Multivariate analysis was performed using selected epidemiological analysis methods of multivariate logistic regression to identify independent risk factors and assess their impact on the occurrence of chronic diseases. At the same time, the interactions between risk factors were analyzed to determine whether they jointly affect the occurrence of chronic diseases;

[0090] (5) Conduct model verification and evaluation.

[0091] S202: Based on the identified risk factors and the weights of each risk factor, a chronic disease health risk assessment model is established using statistical strategies to determine the parameters and assessment criteria of the chronic disease health risk assessment model.

[0092] The specific steps of using statistical strategies to establish a chronic disease health risk assessment model include:

[0093] (1) According to the preprocessed health data X = {x1, x2, ..., x i} characteristics and the nature of the prediction problem, choose the logistic regression model;

[0094] (2) According to the weight of risk factors w j , the identified risk factors were screened using the chi-square test, and the variables with no significant association with the occurrence of the disease were removed, and the remaining variables were used as independent variables in the logistic regression model;

[0095] (3) Using the selected independent and dependent variables, construct a logistic regression model;

[0096] (4) Calculate the parameter values ​​of the logistic regression model according to the parameter estimation method of the logistic regression model;

[0097] (5) Use cross-validation to verify the constructed logistic regression model, and apply the established logistic regression model to the prediction and analysis of actual data to assess the chronic disease risk of individuals or groups.

[0098] The specific steps of step S3 include:

[0099] S301: The pre-processed health data X = {x1, x2, ..., x i}The data is divided into k subsets through data segmentation, and k-1 subsets are used as training sets, and the remaining subset is used as a validation set to train the chronic disease health risk assessment model;

[0100] S302: Calculate the risk probability or level of chronic diseases based on the calculation results of the chronic disease health risk assessment model. The formula is:

[0101]

[0102] Among them, P(X=m+1|Y) represents the probability of chronic disease occurrence under given risk factor Y;

[0103] S303: Verify the trained chronic disease health risk assessment model and calculate the performance index Q = {Acc, S, Sc, Pr, F1, AUC} of the chronic disease health risk assessment model, where Acc represents accuracy, S represents sensitivity, Sc represents specificity, Pr represents precision, F1 represents F1 score, and AUC represents the area under the receiver operating characteristic curve;

[0104] The formula of performance index Q = {Acc, S, Sc, Pr, F1, AUC} is:

[0105]

[0106] Among them, TP represents the number of samples that are actually healthy data and predicted to be healthy data, TN represents the number of samples that are actually diseased data and predicted to be diseased data, FP represents the number of samples that are actually diseased data but predicted to be healthy data, and FN represents the number of samples that are actually healthy data but predicted to be diseased data. AUC is evaluated by plotting the true positive rate and false positive rate curves under different thresholds and calculating the area under them. The closer the AUC value is to 1, the better the model performance is.

[0107] S304: Repeat S302-S303 until each subset is used as a validation set once, and obtain the average value of the performance indicator for each validation. Get the overall performance evaluation of the chronic disease health risk assessment model;

[0108] S305: If the overall performance evaluation result of the chronic disease health risk assessment model meets the requirements, the weight w of each risk factor is j Adjust according to percentage a to get the adjusted risk factor weight w′ j And performance index Q′, calculate the performance index changes of the chronic disease health risk assessment model under different risk factor values, and evaluate the sensitivity of the chronic disease health risk assessment model to each risk factor. The formula is:

[0109]

[0110] Among them, Y′ represents the adjusted risk factor value, P′(X=m+1|Y′) represents the probability of chronic disease occurrence under the adjusted risk factor Y′, Indicates the change value of performance index;

[0111] S306: Based on the results of sensitivity analysis, determine the impact of each risk factor on the prediction results of the chronic disease health risk assessment model. If the adjustment of the risk factor leads to a change in the performance indicator value If the value is larger, it is considered that the chronic disease health risk assessment model is more sensitive to this risk factor.

[0112] The risk level of chronic diseases in step S3 includes three levels: high risk, medium risk, and low risk, and the classification criteria are:

[0113]

[0114] Among them, low, middle, and high represent low risk, medium risk, and high risk, respectively.

[0115] The risk factors in step S2 include lifestyle, environmental factors, genetic factors and psychological state.

[0116] The health management plan in step S4 includes lifestyle adjustment, regular monitoring, and preventive intervention.

[0117] Example 2

[0118] See also Figure 3 Another embodiment provided by the present invention is a chronic disease health risk assessment system, comprising:

[0119] Questionnaire module, risk calculation module, assessment report module, health management module, data update and monitoring module;

[0120] The questionnaire module is used to collect chronic disease health risk data;

[0121] The risk calculation module is used to calculate the risk of the person being assessed developing a chronic disease within a certain period of time in the future;

[0122] The assessment report module is used to present the risk assessment results to the user in the form of a report;

[0123] The health management module is used to provide users with personalized health management plans;

[0124] The data updating and monitoring module is used to regularly update and monitor individual health data and evaluate the performance of the model.

[0125] The risk calculation module includes: a data processing unit and a risk assessment algorithm unit;

[0126] The data processing unit is responsible for cleaning, converting and standardizing the collected data;

[0127] The risk assessment algorithm unit is used to calculate and assess the risk of an individual or a group suffering from a chronic disease.

[0128] The evaluation report module includes: a report generation unit and a report interpretation unit;

[0129] The report generating unit is used to generate an evaluation report according to the calculation results;

[0130] The report interpretation unit is used to interpret and explain the calculated risk assessment results, analyze the factors affecting health risks, and provide personalized health guidance.

[0131] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are within the protection of the present invention.

Claims

1. A chronic disease health risk assessment method, characterized in that: include: Step S1: Determine the goal, scope and type of chronic diseases to be assessed for health risk assessment, collect health data of target individuals or groups from medical records, follow-up medical scales, health examinations, and questionnaires, and pre-process the collected health data; Step S2: Based on the preprocessed health data, the risk factors for chronic diseases are identified through epidemiological algorithms, and a chronic disease health risk assessment model is established using statistical strategies based on the identified risk factors; Step S3: Apply the established chronic disease health risk assessment model to the target individual or group, calculate the risk probability and level of chronic disease according to the calculation results of the chronic disease health risk assessment model, and use cross-validation and sensitivity analysis to verify and calibrate the chronic disease health risk assessment model; Step S4: Interpret and report the individual's chronic disease risk level, health recommendations and management measures, develop a personalized health management plan, and regularly update and monitor the individual's health data to complete early prevention and intervention of chronic diseases.

2. A chronic disease health risk assessment method as claimed in claim 1, characterized in that: The specific steps of preprocessing the collected health data in step S1 include: S101: Use programming languages ​​to remove duplicate, erroneous or incomplete data and convert continuous variables into categorical variables by equal interval grouping; S102: Encode categorical variables, where male is represented by 1 and female is represented by 0; S103: Standardize the encoded data.

3. A chronic disease health risk assessment method as claimed in claim 2, characterized in that: The specific steps of step S2 include: S201: Set the pre-processed health data to X = {x1, x2, ..., x i }, using epidemiological algorithms to analyze X={x1,x2,...,x i }, identify the risk factors associated with the occurrence of chronic diseases Y = {y j },j=1,...,4, determine each risk factor y j The weight w j , where x i represents the type of health data after preprocessing, i represents the number of types of health data after preprocessing and 1≤i≤5, j represents the number of types of risk factors, y j Represents the type of risk factor. The specific formula is: Where h(t,Y) represents the risk factor Y={y j }, j = 1, ..., 4, h0(t) represents the basic risk function, λ j represents the coefficient estimated by regression analysis, exp(·) represents the exponential function, m represents the mth risk factor, and ln(·) represents the logarithmic function; S202: Based on the identified risk factors and the weights of each risk factor, a chronic disease health risk assessment model is established using statistical strategies to determine the parameters and assessment criteria of the chronic disease health risk assessment model.

4. A chronic disease health risk assessment method as claimed in claim 3, characterized in that: The specific steps of step S3 include: S301: The pre-processed health data X = {x1, x2, ..., x i }The data is divided into k subsets through data segmentation, and k-1 subsets are used as training sets, and the remaining subset is used as a validation set to train the chronic disease health risk assessment model; S302: Calculate the risk probability or level of chronic diseases based on the calculation results of the chronic disease health risk assessment model. The formula is: Among them, P(X=m+1|Y) represents the probability of chronic disease occurrence under given risk factor Y; S303: Verify the trained chronic disease health risk assessment model and calculate the performance index Q = {Acc, S, Sc, Pr, F1, AUC} of the chronic disease health risk assessment model, where Acc represents accuracy, S represents sensitivity, Sc represents specificity, Pr represents precision, F1 represents F1 score, and AUC represents the area under the receiver operating characteristic curve; S304: Repeat S302-S303 until each subset is used as a validation set once, and obtain the average value of the performance indicator for each validation. Get the overall performance evaluation of the chronic disease health risk assessment model; S305: If the overall performance evaluation result of the chronic disease health risk assessment model meets the requirements, the weight w of each risk factor is j Adjust according to percentage a to get the adjusted risk factor weight w′ j And performance index Q′, calculate the performance index changes of the chronic disease health risk assessment model under different risk factor values, and evaluate the sensitivity of the chronic disease health risk assessment model to each risk factor. The formula is: Among them, Y′ represents the adjusted risk factor value, P′(X=m+1|Y′) represents the probability of chronic disease occurrence under the adjusted risk factor Y′, Indicates the change value of performance index; S306: Based on the results of sensitivity analysis, determine the impact of each risk factor on the prediction results of the chronic disease health risk assessment model. If the adjustment of the risk factor leads to a change in the performance indicator value If the value is larger, it is considered that the chronic disease health risk assessment model is more sensitive to this risk factor.

5. A chronic disease health risk assessment method as claimed in claim 4, characterized in that: The risk level of chronic diseases in step S3 includes three levels: high risk, medium risk, and low risk, and the classification criteria are: Among them, low, middle, and high represent low risk, medium risk, and high risk, respectively.

6. A chronic disease health risk assessment method as claimed in claim 5, characterized in that: The risk factors in step S2 include lifestyle, environmental factors, genetic factors and psychological state.

7. A chronic disease health risk assessment method according to claim 6, characterized in that: The health management plan in step S4 includes lifestyle adjustment, regular monitoring, and preventive intervention.

8. A chronic disease health risk assessment system, which is implemented based on a chronic disease health risk assessment method according to any one of claims 1 to 7, characterized in that: include: Questionnaire module, risk calculation module, assessment report module, health management module, data update and monitoring module; The questionnaire module is used to collect chronic disease health risk data; The risk calculation module is used to calculate the risk of the person being assessed developing a chronic disease within a certain period of time in the future; The assessment report module is used to present the risk assessment results to the user in the form of a report; The health management module is used to provide users with personalized health management plans; The data updating and monitoring module is used to regularly update and monitor individual health data and evaluate the performance of the model.

9. A chronic disease health risk assessment system as claimed in claim 8, characterized in that: The risk calculation module includes: a data processing unit, a risk assessment algorithm unit, and the assessment report module includes: a report generation unit, a report interpretation unit; The data processing unit is responsible for cleaning, converting and standardizing the collected data; the risk assessment algorithm unit is used to calculate and assess the risk of chronic diseases of individuals or groups; the report generation unit is used to generate an assessment report based on the calculation results; the report interpretation unit is used to explain and illustrate the calculated risk assessment results, analyze the factors affecting health risks, and provide personalized health guidance.

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