Explainable AI achieves 99% accuracy in Chronic Kidney Disease prediction

Researchers have developed an explainable AI (XAI) model using simulated federated learning to predict Chronic Kidney Disease (CKD). The model, which integrates Random Forest, AdaBoost, and XGBoost algorithms, achieved an average accuracy of 99% on a clinical dataset. This approach aims to improve early CKD detection and enhance the transparency of AI in healthcare. AI

IMPACT Enhances transparency and accuracy in medical diagnostics, potentially improving patient outcomes through early disease detection.

RANK_REASON The cluster contains an academic paper detailing a new AI methodology and its application. [lever_c_demoted from research: ic=1 ai=1.0]

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• AdaBoost
• arXiv
• chronic renal insufficiency
• explainable AI
• federated learning
• GridSearchCV
• random forest
• VotingClassifier
• XGBoost

• paper
• safety

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