Chronic Disease Risk Prediction And Package Optimization Based On Health Examination Data

In recent years,China has initiated a “supply-side” reform in the field of health care,established the Health and Wellness Committee,and introduced guiding policies and regulations such as “Healthy China 2030” and the family doctor contract model in an effort to establish a wider range of human health service system.Health checkups are basic health services for the entire population of society.At present,there are two main problems faced by research based on health checkup data.First,with the popularization of health checkup services,the coverage and amount of the health checkup data have become increasingly large,but it still lacks a direct correlation with the disease and has a low positive sample rate and sparse features.It is often difficult to obtain good accuracy when using machine learning methods to predict disease risk.Second,as disease screening technologies continue to update,the health portfolio of medical examination items is becoming more and more abundant,and the price difference is getting widening.Generally,the design methods of health medical examination items that rely on medical experience are no longer applicable.It is difficult to identify the most informative and comprehensive physical examination items,which reduces the benefits of health checkups.To solve the above problems,this study focuses on the medical history records of the health examination data and the characteristics and distribution of the patient's medical history are summarized to establish the association between the health examination data and the characteristics of 6 chronic diseases;considering the large quantity,low positive sample rate,and sparse feature characteristics of the health examination data,the LightGBM algorithm,Gradient-based One-Side Sampling(GOSS),and Exclusive Feature Binding(EFB)technology are applied to establish a chronic disease risk prediction model based on health examination data,and the results were compared with those of the XGBoost algorithm;this study further proposes a method for ranking physical examination items based on feature importance and price,and 100 features most associated with the prediction of chronic disease risk are extracted and classified into 3 groups and 42 examination items,which have achieved the optimization of health examination items;the characteristics and laws of health checkup items are finally summarized and the general principles of health checkup items selection are provided,which can assist the design of checkup packages for family doctors,staff checkups,and chronic disease checkups.In this study,a method for predicting the risk of chronic diseases suitable for healthy people is established.By comparing with XGBoost algorithm,it has been proved that the method proposed by this study has a better prediction accuracy,and besides interpreting abnormal data,it is also a novel effective method for analyzing physical examination data.As for the issues that health checkup package relies on subjective knowledge such as expert experience and questionnaires,and lacks of systemic,a method for sequencing health examination items based on feature importance and price influencing factors has been proposed to optimize the health checkup items and accomplish the application designed of health checkup packages for different groups.