Supplementary file 3_The application of explainable machine learning for the identification of MASLD in non-obese populations.docx

Model performance was evaluated using AUC, calibration analysis and decision curve analysis (DCA). SHAP analysis was applied to interpret the optimal models.</p>Results<p>LASSO regression identified eight factors: apolipoprotein B, triglycerides, fasting insulin, fasting plasma glucose, waist circumference, age, alkaline phosphatase, and systolic blood pressure. On the test set, GBM achieved the highest AUC of 0.779 (95% CI: 0.695–0.863), followed by NN (0.777) and LDA (0.771).

Both GBM and LDA demonstrated acceptable calibration (Hosmer–Lemeshow p > 0.05) and clinical utility. SHAP analysis revealed that fasting insulin was the most influential indicator for LDA, while triglycerides dominated GBM case identification.</p>Conclusion<p>This study developed interpretable machine learning models (LDA and GBM) for identifying MASLD in non-obese individuals using readily available clinical parameters.

The models demonstrated acceptable discrimination and calibration, with SHAP analysis highlighting the central roles of insulin resistance and dyslipidemia in the pathogenesis of non-obese MASLD.</p>