EndoStack-XAI: A Hybrid and Explainable Machine Learning Framework for Endometriosis Prediction

This study proposes EndoStack-XAI, a leakage-safe two-level stacking ensemble for interpretable endometriosis prediction using demographic, reproductive, symptom-related, laboratory, and clinical features. The framework combines RBF-SVM, Random Forest, and Extra Trees as base learners with Logistic Regression as a meta-learner trained on leakage-controlled out-of-fold predictions. SHAP and LIME are further integrated to provide global and local model interpretability while maintaining strict separation of the held-out test set from model development.