Machine learning prediction of metabolic dysfunction-associated fatty liver disease risk in American adults using body composition: explainable analysis based on SHapley Additive exPlanations

Machine learning prediction of metabolic dysfunction-associated fatty liver disease risk in American adults using body composition: explainable analysis based on SHapley Additive exPlanations

BackgroundMetabolic dysfunction-associated fatty liver disease (MAFLD) is a prevalent and progressive liver disorder closely linked to obesity and metabolic dysregulation. Traditional anthropometric measures such as body mass index (BMI) are limited in their ability to capture fat distribution and a...

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Bibliographic Details
Main Authors: Yan Hong, Xinrong Chen, Ling Wang, Fan Zhang, ZiYing Zeng, Weining Xie
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Nutrition
Subjects:
MAFLD
body composition
machine learning
SHAP
NHANES
Online Access:https://www.frontiersin.org/articles/10.3389/fnut.2025.1616229/full
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