Explainable AI for Chronic Kidney Disease Prediction Using Simulated Federated Learning

2026-07-28

AI Summary

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.

Abstract

Chronic Kidney Disease (CKD), characterized by the gradual loss of kidney function, remains a significant public health challenge. Early detection is crucial for preventing severe complications and enhancing patient outcomes. In this study, Federated Learning (FL) with a VotingClassifier was used to predict CKD using a clinical dataset, where Random Forest, AdaBoost, and XGBoost were utilized to compare and identify the best-fitting model for the global server. Additionally, GridSearchCV was applied to optimize the models' performance on the client's side. To enhance model transparency and trustworthiness, explainable AI (XAI) techniques were incorporated to interpret the prediction mechanisms. The global model's average accuracy was 99%, highlighting the potential of interpretable FL models in supporting early CKD diagnosis and advancing data-driven healthcare solutions.

Problem

• Chronic Kidney Disease
• Early Prediction
• Explainable AI
• Federated Learning
• Model Interpretability

Innovation

• Explainable AI
• Federated Learning
• Chronic Kidney Disease
• VotingClassifier
• GridSearchCV