Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis.

Journal: BMC medical informatics and decision making
Published Date: Aug 1, 2022

Abstract

BACKGROUND: Kidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using Machine Learning (ML) algorithms have been established in nephrology. However, their accuracy have been inconsistent. Therefore, we conducted a systematic review and meta-analysis to investigate the diagnostic accuracy of ML algorithms for kidney disease progression.

Authors

  • Nuo Lei
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Xianlong Zhang
    Department of Orthopaedics, Shanghai Jiaotong University Affiliated Shanghai Sixth People's Hospital, Shanghai, 200233, P.R.China.
  • Mengting Wei
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Beini Lao
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Xueyi Xu
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Min Zhang
    Department of Infectious Disease, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Huifen Chen
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Yanmin Xu
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Bingqing Xia
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Dingjun Zhang
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Chendi Dong
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Lizhe Fu
    Chronic Disease Management Department, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Fang Tang
  • Yifan Wu
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.

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