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This study employs Federated Learning (FL) combined with a VotingClassifier to predict Chronic Kidney Disease (CKD) using clinical datasets, aiming to enhance early detection and patient outcomes. By comparing Random Forest, AdaBoost, and XGBoost models, the research identifies the optimal model for a global server, achieving an impressive average accuracy of 99%. The incorporation of explainable AI (XAI) techniques not only boosts model transparency but also fosters trust in the predictive mechanisms, marking a significant advancement in data-driven healthcare solutions.
Achieving 99% accuracy in CKD prediction through federated learning while ensuring model interpretability could transform early diagnosis in healthcare.
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.