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Explainable AI achieves 99% accuracy in Chronic Kidney Disease prediction

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Explainable AI achieves 99% accuracy in Chronic Kidney Disease prediction

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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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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Md Zahid Hasan Ontor, Md Al Amin, Anik Dev Nath, Bikash Kumar Paul ·

    Explainable AI for Chronic Kidney Disease Prediction Using Simulated Federated Learning

    arXiv:2607.25348v1 Announce Type: cross 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 t…

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