Development and validation of a machine learning model for identifying liver fibrosis in MAFLD: a retrospective model development study.

backgroundMetabolic dysfunction-associated fatty liver disease (MAFLD), as the most prevalent chronic liver disease globally, is closely related to the prevalence of metabolic syndrome. Liver fibrosis is a core stage in the progression of this disease, significantly increasing the risk of liver cirrhosis, liver failure, and hepatocellular carcinoma. Early identification of high-risk patients is crucial for blocking disease progression. We developed and internally validated a machine learning model to identify MAFLD patients with VCTE-defined liver fibrosis using routinely collected clinical variables.

methodThis study selected 2728 research subjects from January 2020 to October 2025. Through the least absolute shrinkage and selection operator (LASSO) regression, Boruta algorithm, and recursive feature elimination (RFE), eight key variables, namely ALT, AST, SBP, BMI, WHR, DBP, LDL-C, and GGT, were selected. Classification models employed logistic regression, decision trees, random forests, XGBoost, LightGBM, support vector machines (SVM), and artificial neural networks (ANN). Nomogram and SHapley additive exPlanations (SHAP) were used to explain the constructed model.

resultsThis study included 2728 patients with 1013 cases (38%) of liver fibrosis. XGBoost, LightGBM, and Random Forest demonstrated broadly comparable performance.

conclusionThe proposed machine learning model has clinical application potential in identifying liver fibrosis defined by VCTE in patients with MAFLD. External validation and prospective clinical evaluation are required before routine clinical implementation.