Artificial Intelligence-based Prediction of Diabetes and Prediabetes Using Health Checkup Data in Korea

Abstract

The economic burden of Type 2 Diabetes (T2D) on society has increased over time. Early prediction of diabetes and prediabetes can reduce treatment cost and improve intervention. The development of (pre)diabetes is associated with various health conditions that can be monitored by routine health checkups. This study aimed to develop amachine learning-based model for predicting (pre)diabetes. Our frameworks were based on 22,722 patient samples collected from 2013 to 2020 in ageneral hospital in Korea. The disease progression was divided into three categories based on fasting blood glucose: normal, prediabetes, and T2D. The risk factors at each stage were identified and compared. Based on the area under the curve, the support vector machine appeared to have optimal performance. At the normal and prediabetes stages, fasting blood glucose and HbA1c are prevalent risk features for the suggested models. Interestingly, HbA1c had the highest odds ratio among the features even in the normal stage (FBG is less than 100). In addition, factors related to liver function, such as gamma-glutamyl transpeptidase can be used to predict progression from normal to prediabetes, while factors related to renal function, such as blood urea nitrogen and creatinine, are prediction factors of T2D development.