Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults

Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD). We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40-80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004-2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR)