Development and validation of a machine learning-based framework for assessing metabolic-associated fatty liver disease risk.

Journal: BMC public health
Published Date: Sep 18, 2024

Abstract

BACKGROUND: The existing predictive models for metabolic-associated fatty liver disease (MAFLD) possess certain limitations that render them unsuitable for extensive population-wide screening. This study is founded upon population health examination data and employs a comparison of eight distinct machine learning (ML) algorithms to construct the optimal screening model for identifying high-risk individuals with MAFLD in China.

Authors

  • Jiale Deng
    Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, Guangdong, China.
  • Weidong Ji
    Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, Guangdong, China.
  • Hongze Liu
    Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, Guangdong, China.
  • Lin Li
    Department of Medicine III, LMU University Hospital, LMU Munich, Munich, Germany.
  • Zhe Wang
    Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China.
  • Yurong Hu
    School of Computer Science, China University of Geosciences, Wuhan, Beihe, 430074, China.
  • YuShan Wang
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, 39 Huaxiang Road, Shenyang, 110021, People's Republic of China.
  • Yi Zhou
    Eye Center of Xiangya Hospital, Central South University, Changsha, Hunan, China.

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