Machine Learning Models for Predicting Type 2 Diabetes Complications in Malaysia

Abstract

This study aimed to develop machine learning (ML) models to predict diabetic complications in patients with type 2 diabetes (T2D) in Malaysia. Data from the Malaysian National Diabetes Registry and Death Register were used to develop predictive models for five complications: all-cause mortality, retinopathy, nephropathy, ischemic heart disease (IHD), and cerebrovascular disease (CeVD). Accurate predictions may enable targeted preventive intervention and optimal disease management. The cohort comprised 90 933 T2D patients treated at public health clinics in southern Malaysia from 2011 to 2021. Seven ML algorithms were tested, with the Light Gradient Boosting Machine (LGBM) demonstrating the best performance. LGBM models achieved ROC-AUC scores of 0.84 for all-cause mortality, 0.71 for retinopathy, 0.71 for nephropathy, 0.66 for IHD, and 0.74 for CeVD. These findings support integrating ML models, particularly LGBM, into clinical practice for predicting diabetes complications. Further optimization and validation are necessary to enhance applicability across diverse populations.