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

ABSTRACT:

Background

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).

Methods

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) <60 mL/min/1.73 m² with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, w

SUBMITTER: Sabanayagam C 

PROVIDER: S-EPMC10531395 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature