Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES.

Journal: BMC gastroenterology
PMID: 40375096

Abstract

BACKGROUND: The mortality burden of metabolic dysfunction-associated fatty liver disease (MAFLD) is rising, making it crucial to predict mortality and identify the factors influencing it. While advanced machine learning algorithms are gaining recognition as effective tools for clinical prediction, their ability to predict all-cause mortality of MAFLD individuals remains uncertain. This study aimed to develop different machine learning models to predict all-cause mortality of MAFLD individuals, compare the predictive performance of these models, and identify the risk factors contributing all-cause mortality, which is crucial for management of MAFLD individuals.

Authors

  • Xueni Wang
    Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Huihui Chen
    School of Education Science, Jiangsu Normal University, Xuzhou, China.
  • Luqiao Wang
    School of Computer Science and Technology, Xidian University, Xi'an, 710071, China; Intelligent Financial Software Engineering New Technology Joint Laboratory, Xidian University, Xi'an, 710071, China. Electronic address: wangluqiao@stu.xidian.edu.cn.
  • Wenguang Sun

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