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March 26, 2026Sci0 citationsOpen Access

Exploring Machine Learning Classifiers for Chronic Kidney Disease Diagnosis

Review on Exploring Machine Learning Classifiers in the Diagnosis of Chronic Kidney Disease

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Authors

SBSonam BhandurgeKSKuldeep SambrekarRMRashmi Laxmikant Malghan

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Overview

Review evaluates machine learning classifiers for chronic kidney disease diagnosis, highlighting implications for healthcare solutions.

Key Points

  • The aim is to evaluate the effectiveness of different machine learning classifiers for diagnosing chronic kidney disease.
  • Review of various machine learning models
  • Data sourced from UCI and self-collected datasets
  • Comparison of performance among ML classifiers
  • Focus on transparent and interpretable models
  • Ensemble methods demonstrated the highest performance in CKD classification
  • Challenges identified in model integration and interpretability
  • Emphasis on the need for reliable and efficient models for clinical application

Cite This Study

Bhandurge et al. (2026) studied this question.

synapsesocial.com/papers/69c4cda5fdc3bde44891a4b9https://doi.org/10.3390/sci8040068
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Clinical Application of Machine Learning Models for Early-Stage Chronic Kidney Disease Detection2025 · 25 citations
  2. 2Chronic Kidney Disease Prediction using Machine Learning2024 · 1 citations
  3. 3Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence2026
  4. 4Class-Imbalance Aware Machine Learning for CKD Detection and Risk Assessment2024
  5. 5Predictive Analysis of Chronic Kidney Disease in Machine Learning2025 · 1 citations