The accuracy of Machine learning in the prediction and diagnosis of diabetic kidney Disease: A systematic review and Meta-Analysis.

Abstract

Machine learning (ML) has gained attention in diabetes management, particularly for predicting and diagnosing diabetic kidney disease (DKD). However, systematic evidence on its performance remains limited. This study evaluates the predictive and diagnostic accuracy of ML in DKD to support the development of tailored prevention strategies and non-invasive diagnostic tools. A systematic search of PubMed, Embase, Web of Science, and Cochrane (up to April 14, 2024) identified relevant studies. Risk of bias was assessed using tools for predictive models, and meta-analysis included subgroup analyses based on task type, dataset, and model type. A total of 34 studies were included, with 19 on DKD risk prediction and 15 on diagnosis. For prediction, the pooled c-index was 0.81 (95% CI 0.79-0.83), sensitivity 0.81 (95% CI 0.74-0.86), and specificity 0.82 (95% CI 0.73-0.89). For diagnosis, the pooled c-index was 0.81 (95% CI 0.79-0.83), sensitivity 0.81 (95% CI 0.78-0.84), and specificity 0.75 (95% CI 0.72-0.79). ML shows promising accuracy in DKD prediction and diagnosis, offering a viable tool for early screening and risk assessment.