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

Journal: International journal of medical informatics
Published Date: May 17, 2025

Abstract

PURPOSE: 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.

Authors

  • Changmao Dai
    Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, 61000, China.
  • Xiaolan Sun
    Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, 61000, China.
  • Jia Xu
    Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing, 211816, P.R. China.
  • Maojun Chen
    Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, 61000, China.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
  • Xueping Li
    Department of Industrial and Systems Engineering, The University of Tennessee, Knoxville, TN 37902, USA.

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