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Daffodil International University

Academic institutionasia · bd
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Representative Papers

Explainable AI for Chronic Kidney Disease Prediction Using Simulated Federated Learning

Jul 28, 2026

This study addresses the challenges of early diagnosis of chronic kidney disease (CKD) and the limitations imposed by data silos and privacy constraints in multi-institutional healthcare settings. To overcome these issues, the authors propose an interpretable federated learning framework that integrates explainable artificial intelligence (XAI) with a voting-based ensemble strategy. The approach combines Random Forest, AdaBoost, and XGBoost into a VotingClassifier and employs GridSearchCV for hyperparameter optimization, enabling collaborative model training across institutions without compromising patient privacy. Experimental results demonstrate that the resulting global model achieves an average accuracy of 99%, substantially enhancing early diagnostic performance. This work represents the first successful implementation of a CKD prediction system that simultaneously ensures privacy preservation and model interpretability, thereby validating the feasibility and advantages of interpretable federated learning in clinical applications.

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Latest Papers

Explainable AI for Chronic Kidney Disease Prediction Using Simulated Federated Learning

Jul 28, 2026

This study addresses the challenges of early diagnosis of chronic kidney disease (CKD) and the limitations imposed by data silos and privacy constraints in multi-institutional healthcare settings. To overcome these issues, the authors propose an interpretable federated learning framework that integrates explainable artificial intelligence (XAI) with a voting-based ensemble strategy. The approach combines Random Forest, AdaBoost, and XGBoost into a VotingClassifier and employs GridSearchCV for hyperparameter optimization, enabling collaborative model training across institutions without compromising patient privacy. Experimental results demonstrate that the resulting global model achieves an average accuracy of 99%, substantially enhancing early diagnostic performance. This work represents the first successful implementation of a CKD prediction system that simultaneously ensures privacy preservation and model interpretability, thereby validating the feasibility and advantages of interpretable federated learning in clinical applications.

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