A Hybrid ReliefF-Wrapper Feature Optimization Framework with Stacking Ensembles for Explainable Breast Cancer Diagnosis

The significant research contributions of this study are threefold: first, a two-stage Hybrid Feature Selection (HFS) strategy combining statistical filtering (ReliefF) with non-linear wrapper selection (Random Forest Gini importance) is implemented to prune the high-dimensional feature space down to the top optimal attributes, eliminating redundancy and reducing computational overhead; second, an optimized Stacked Generalization architecture fuses diverse $L_0$ base learners (Random Forest, XGBoost, and Support Vector Machine) with an $L_1$ Logistic Regression meta-learner, achieving superior predictive performance on the WDBC test set ($97.67\%$ Accuracy, $100.00\%$ Precision, $93.75\%$ Recall, $96.77\%$ F1-Score, and $99.83\%$ ROC-AUC); and third, a dual-level Explainable AI (XAI) framework integrating SHAP for global cohort feature attributions and LIME for local patient-level explanations resolves the “black-box” nature of ensemble models to provide transparent, clinically actionable diagnostic decision support.