Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.

Journal: Archives of Iranian medicine
PMID: 39492562

Abstract

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. This study aimed to develop and validate machine-learning (ML) algorithms for accurate MASLD screening in a geographically diverse, large-scale population.

Authors

  • Fatemeh Masaebi
    Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Mehdi Azizmohammad Looha
    Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Morteza Mohammadzadeh
    Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
  • Vida Pahlevani
    Department of Biostatistics, Faculty of Medical Science, Tarbiat Modares University, Tehran, Iran.
  • Mojtaba Farjam
    Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran.
  • Farid Zayeri
    Proteomics Research Center and Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Reza Homayounfar
    National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

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