Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.

Journal: Frontiers in endocrinology
PMID: 40099258

Abstract

BACKGROUND: Machine learning (ML) models are being increasingly employed to predict the risk of developing and progressing diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). However, the performance of these models still varies, which limits their widespread adoption and practical application. Therefore, we conducted a systematic review and meta-analysis to summarize and evaluate the performance and clinical applicability of these risk predictive models and to identify key research gaps.

Authors

  • Yihan Li
    Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China.
  • Nan Jin
    Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China.
  • Qiuzhong Zhan
    Faculty of Chinese Medicine, Macau University of Science and Technology, Macao,  Macao SAR, China.
  • Yue Huang
    Xiamen University, Xiamen, Fujian 361005, China.
  • Aochuan Sun
    Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China.
  • Fen Yin
    Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China.
  • Zhuangzhuang Li
    College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
  • Jiayu Hu
    Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China.
  • Zhengtang Liu
    Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China.

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