XAI-Enhanced Hybrid Models for Effective Chronic Kidney Disease Prediction

Our key contribution lies in developing a hybrid predictive framework that combines machine learning, deep learning, and ensemble methods for highly accurate and interpretable Chronic Kidney Disease (CKD) detection. We addressed data imbalance through SMOTE, SMOTE-Tomek, and SMOTE-ENN, followed by SHAP-based feature selection via XGBoost, where top-ranked features were aggregated across all resampling strategies. Complementing this, statistical analysis with SPSS reinforced the identification of clinically significant features. Multiple machine learning classifiers, ensemble approaches, and advanced deep learning models—including Attention Autoencoder with XGBoost, TabNet, TabPFN, LightCNN, MLP, and DeepCrossNet—were systematically evaluated. DeepCrossNet achieved 97.38% accuracy, while the stacking ensemble attained 97.50% and Random Forest reached 97.71%. Furthermore, SHAP and LIME explanations emphasized GFR and serum creatinine as critical predictors, enhancing clinical trust. Visualization with t-SNE and UMAP confirmed class separability and detected ambiguous cases. Together, these contributions highlight the novelty of integrating SHAP-aggregated features, advanced resampling, and hybrid ML-DL ensembles to advance both accuracy and interpretability in CKD prediction.