Chronic Disease Prediction Using Common Data Model (Preprint)

Abstract

BACKGROUND Chronic disease management is a major health issue worldwide. OBJECTIVE This study suggests the possibility of preemptive management of chronic diseases by predicting the occurrence of chronic diseases using CDM and machine learning. In this study, four major chronic diseases, namely, diabetes, hypertension, hyperlipidemia, and cardiovascular disease, were selected and a model for predicting their occurrence within 10 years was developed. METHODS We used 4 algorithms to predict disease occurrence. RESULTS XGBoost presented the highest predictive performance for the 4 diseases (diabetes, hypertension, hyperlipidemia, cardiovascular disease) of 80% or more —0.84 to 0.93 in AUC standards—showing the best performance. CONCLUSIONS Through the chronic disease prediction machine learning model developed in this study using RWD-based CDM, even with the National Health Insurance Corporation examination data that can be easily obtained by individuals, the risk of major chronic diseases within 10 years Demonstrate that you can specifically identify your health risk factors.