Analysis of Health Screening Records Using Interpretations of Predictive Models

Health screening is conducted in many countries to track general health conditions and find asymptomatic patients. In recent years, large-scale data analyses on health screening records have been utilized to predict patients' future health conditions. While such predictions are significantly important, it is also of great interest for medical researchers to identify factors that could deteriorate patients' medical conditions in the future. For this purpose, we propose to use interpretations of trained predictive models. Specifically, we trained machine learning models to predict future diabetes stages, then applied permutation importance, SHapley Additive exPlanations (SHAP), and a sensitivity analysis to extract features that contribute to aggravation. Among the trained models, XGBoost performed best in terms of the Matthews correlation coefficient. Permutation importance and SHAP showed that the model makes good predictions using a number of attributes conventionally known to be related to diabetes, but also those not commonly used in the diagnosis of diabetes. A sensitivity analysis showed that the predictions' changes were mostly consistent with our intuition on how daily behavior affects type 2 diabetes's aggravation.