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Updated: Apr 4, 2026
Mouse Model of Metabolic Dysfunction-Associated Steatotic Liver Disease with Fibrosis
Published on: July 18, 2025
Machine Learning Models to Predict Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) With Simple
Shiying Du1, Hailiang Yu2, Jianbo Du1
1Comprehensive Supervision and Service Center of Hebei Health Commission, Shijiazhuang, China.
Background:
Metabolic dysfunction-associated steatotic liver disease (MASLD) is an emerging global health concern. This study was aimed at exploring the feasibility of utilizing machine learning (ML) algorithms to predict MASLD in large general populations based on simple anthropometric and biochemical parameters.
Methods:
Data from the 2017-2020 cycles of the US National Health and Nutrition Examination Survey (NHANES) were utilized. A total of 6814 participants (53.0% female) with complete transient elastography data were included. MASLD was defined as a controlled attenuation parameter ≥ 280 dB/m, with cardiometabolic risk factor and without excessive alcohol use. Key characteristics and biomarkers associated with MASLD were identified using the least absolute shrinkage and selection operator (LASSO) and the Boruta algorithms. ML methods, including logistic regression (LR), extreme gradient boosting (XGBoost), bootstrap aggregating, random forest, naive Bayes, light gradient boosting machine (LightGBM), decision tree, and support vector machines, were employed to develop the MASLD prediction models.
Results:
The median age of the 6814 participants was 53 years (interquartile range: 37~65). MASLD was detected among 2611 (38.3%) participants. Key predictors selected via LASSO and Boruta algorithms included body weight, standing height, waist circumference, diagnosis of diabetes, alanine aminotransferase, aspartate aminotransferase, and gamma glutamyl transferase. The areas under the receiver operating characteristic curves of LR, XGBoost, and other ML models were 0.841, 0.837, 0.815, 0.838, 0.814, 0.842, 0.796, and 0.828 in the internal validation cohort. Results indicate that LR, XGBoost, and LightGBM models outperform other models in predicting MASLD.
Conclusions:
The ML models of LR, XGBoost, and LightGBM are effective and simplified tools for predicting MASLD in the US general population. This study underscores the potential of ML models with simple noninvasive biomarkers in enhancing early detection and personalized management of fatty liver disease.
