ArticleBMC gastroenterology2025
Machine learning-based disease risk stratification and prediction of metabolic dysfunction-associated fatty liver disease using vibration-controlled transient elastography: Result from NHANES 2021-2023.
Article in BMC gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.Digestive diseases and sciences · 2026Review
- Metabolic dysfunction-associated fatty liver disease in chronic hepatitis B: dual effects on hepatocarcinogenesis and evolving strategies for risk prediction.Frontiers in immunology · 2026Review
- Machine Learning and SHAP Value Interpretation for Predicting Hepatic Steatosis Using Vibration-Controlled Transient Elastography.International journal of endocrinology · 2026Article
- Nutrients as epigenetic modulators in metabolic dysfunction-associated steatotic liver disease.World journal of hepatology · 2025Review
- Gut microbiota and metabolomics in metabolic dysfunction-associated fatty liver disease: interaction, mechanism, and therapeutic value.Frontiers in cellular and infection microbiology · 2025Review
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Authors and funding
5 authors.
Funding
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Abstract
backgroundMetabolic dysfunction-associated fatty liver disease (MAFLD) is a common chronic liver disease and represents a significant public health issue. Nevertheless, current risk stratification methods remain inadequate. The study aimed to use machine learning in the identification of significant features and the development of a predictive model to determine its usefulness in discrimination of MAFLD's risk stratification (low, moderate, and high) in adults.
methodsThe data of the 2021-2023 NHANES database were analyzed. Vibration-controlled transient elastography measurements, including controlled attenuation parameter for the evaluation of steatosis and liver stiffness for the evaluation of fibrosis, were used for risk stratification. The participants were grouped into low-risk, moderate-risk, and high-risk groups based on specific criteria. Feature selection was conducted through Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest classification.
resultsA total of 4,227 participants were included in the study. There were 16 significant predictors identified by LASSO regression, among which the top 10 predictors were demographic (age, gender, race, hypertension history), clinical (body mass index, waist circumference, hemoglobin, glycohemoglobin, lymphocyte count), and education level. The area under the receiver operating characteristic curve (AUC) of the random forest model in the validation set was 0.80, and the individual AUC was 0.83, 0.66 and 0.79 for the low-, moderate-, and high-risk groups, respectively.
conclusionOur machine learning model has excellent performance in stratification of risk for MAFLD with readily available clinical and demographic parameters. This model could be employed as a valuable screening tool to refer high-risk patients for further hepatological evaluation.
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