A privacy preserving federated meta ensemble stacking framework with explainability and differential privacy for chronic kidney disease prediction in resource constrained settings
TL;DR
Findings demonstrate that the proposed framework provides an accurate, privacy-aware, and interpretable solution for decentralized CKD prediction, and maintains robust performance under Gaussian noise.
Abstract
Chronic kidney disease (CKD) is a major public health concern that requires early and reliable diagnosis to reduce disease progression and associated complications. Existing machine learning approaches often rely on centralized training, limiting their applicability in healthcare environments where data privacy, heterogeneity, and resource constraints are critical concerns. To address these challenges, this study proposes a privacy-preserving federated meta-ensemble stacking framework for CKD prediction. The proposed approach integrates a hybrid preprocessing pipeline, federated learning with prediction-level aggregation, differential privacy, and SHAP-based explainability to enable secure, interpretable, and decentralized model learning. Experimental evaluation demonstrates a mean cross-validation accuracy of 98.85% with an F1-score of 98.58%. The framework maintains robust performance under Gaussian noise, achieving 97.50% accuracy, while cross-client evaluation reaches 99.25%, demonstrating strong generalization across distributed healthcare institutions. The model also exhibits excellent discrimination and calibration, achieving a ROC-AUC of 0.998, PR-AUC of 0.997, and a Brier score of 0.00038. Furthermore, communication overhead is reduced from 0.82 MB to 0.25 MB through compression and sparse updates. These findings demonstrate that the proposed framework provides an accurate, privacy-aware, and interpretable solution for decentralized CKD prediction.
