Supplementary_Material_JAAD-D-25-04018
Description
1.Questionnaire assessing patient expectations of treatment outcomes. 2.Methods. 3.Graphical abstract. 4.Raw data. 5.Code. Table S1. Baseline characteristics of PWS patients. Table S2. Hyperparameter tuning details for machine learning models. Table S3. Multivariate logistic regression quantifying mediation effects. Figure S1. Feature selection and model performance comparison: LASSO, RFE, and random forest analyses. Figure S2. Confusion matrices of five models at the optimal threshold (0.515). Figure S3. Performance evaluation of five models: ROC, confusion matrix, calibration, and decision curves. Figure S4. Feature importance and SHAP analysis of the LightGBM model. Figure S5. SHAP summary and dependence plots of the LightGBM model. Figure S6. Exploratory analysis of associations among clinical and dermoscopic variables. Figure S7. Surrogate decision tree of the LightGBM model
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Institutions
- Xi'an Jiaotong University