From Traditional Metabolic Markers to Ensemble Learning: Comparative Application of Machine Learning Models for Predicting NAFLD Risk in Adolescents

Chenming Zhang¹Bin Niu²Rong Wang²Liaoyun Zhang^2*

• ¹Shanxi Medical University, Taiyuan, China
• ²First Hospital of Shanxi Medical University, Taiyuan, China

Background: Non-alcoholic fatty liver disease (NAFLD) is increasingly prevalent among adolescents and poses a major public health concern. Because imaging and biopsy are unsuitable for large-scale screening, there is an urgent need for accurate, non-invasive prediction tools. Methods: Data from 2,132 U.S. adolescents (NHANES 2011–2020) were analyzed. Nine machine learning (ML) models were developed using features selected by Light Gradient Boosting Machine (LightGBM). Performance was assessed by AUC, accuracy, sensitivity, precision, F1-score, and calibration. The Extra Trees (ET) model was further compared with TyG-based logistic regression models. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP), and an interactive online prediction tool was deployed. Results: NAFLD prevalence was 13.0%. The ET model achieved the best overall performance (AUC = 0.784, ACC = 0.773, Kappa = 0.320), outperforming other ML algorithms and TyG-based models, which showed higher sensitivity but poorer precision. SHAP analysis identified waist circumference, triglycerides, insulin, and HDL as key predictors, revealing nonlinear threshold effects. The online tool allows individualized risk estimation based on routine clinical variables. Conclusion: The ET-based ML model provides an accurate and interpretable approach for adolescent NAFLD risk prediction. By surpassing traditional metabolic indicators and offering an accessible web-based calculator, it supports scalable, cost-effective early screening and targeted prevention strategies.