Machine learning-based prediction of persistent diabetic macular edema using OCT-derived structural biomarkers and clinical features

Machine learning models integrating OCT-derived structural biomarkers with clinical features provide superior predictive accuracy for persistent DME compared with conventional models. SHAP-based interpretation consistently identified diabetes duration, EZ/ELM disruption, and HF as key predictors. Among the developed models, SVM achieved the highest AUC in the internal validation cohort, whereas XGBoost and RF provided a favorable balance between predictive performance and interpretability, supporting their potential use in individualized treatment planning.