Incorporating Insulin Resistance Biomarkers into Machine Learning Models Enhances Diagnostic Accuracy for Metabolic Dysfunction–Associated Steatotic Liver Disease

Introduction: Metabolic dysfunction–associated steatotic liver disease (MASLD) has emerged as the most prevalent chronic liver disorder worldwide. Despite its increasing prevalence, MASLD is often asymptomatic in its early course, leading to considerable underdiagnosis. Insulin resistance (IR) plays a key role in MASLD development; however, the incremental value of IR-related biomarkers in machine learning (ML) diagnostic models for MASLD has not been fully investigated. Methods: This retrospective single-center study included 18,535 adults (5992 with MASLD and 12,543 without MASLD) undergoing routine health examinations at our research center. Two feature sets were created: Set 1 included demographic, anthropometric, clinical, and biochemical indicators, while Set 2 further incorporated IR-related biomarkers, including triglyceride–glucose (TyG)–based indices and the metabolic score for insulin resistance (METS-IR). Six ML algorithms were used to develop diagnostic models. Model performance was assessed using sensitivity, specificity, F1-score, and area under the curve (AUC) on the training and validation sets, with clinical utility evaluated using decision curve analysis (DCA). Model interpretability was further explored using Shapley Additive exPlanations (SHAP). Results: All six ML models demonstrated a statistically significant improvement in AUC values after incorporating IR-related biomarkers (all P < 0.05). In the validation set, the extremely randomized trees (ERT) model with Set 2 achieved the highest AUC (0.901). DCA further indicated that models incorporating IR-related biomarkers generally provided a higher net clinical benefit. Overall, the ERT model provided the most favorable combination of discrimination and clinical utility. SHAP analysis further emphasized the important role of IR-related biomarkers in MASLD diagnosis. Conclusion: Incorporating IR-related biomarkers into ML diagnostic models improved the accuracy and clinical utility of MASLD detection without increasing testing costs or patient burden. These findings highlight the incremental diagnostic value of IR-related biomarkers in ML models and support their use as a decision-support tool for MASLD screening in general populations.