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September 10, 2025

Analytical Prediction for Chronic Kidney Disease: A Comparison of Machine Learning Methods

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This analysis compares prediction accuracy of machine learning methods for chronic kidney disease, suggesting optimal early detection tools.

Naive Bayes yielded the highest accuracy of 94.21% in predicting chronic kidney disease.
Random Forest achieved an impressive 90.50% accuracy, indicating strong predictive capabilities.
Three machine learning methods, Random Forest, Naive Bayes, and SVM, were evaluated for chronic kidney disease prediction accuracy.
These prediction models can significantly aid in the early detection and management of chronic kidney disease.

Qodri et al. (2025) studied this question.

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September 10, 2025

View Full Paper

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Authors

This analysis compares prediction accuracy of machine learning methods for chronic kidney disease, suggesting optimal early detection tools.

Naive Bayes yielded the highest accuracy of 94.21% in predicting chronic kidney disease.
Random Forest achieved an impressive 90.50% accuracy, indicating strong predictive capabilities.
Three machine learning methods, Random Forest, Naive Bayes, and SVM, were evaluated for chronic kidney disease prediction accuracy.
These prediction models can significantly aid in the early detection and management of chronic kidney disease.

Qodri et al. (2025) studied this question.

View Full Paper

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Bookmark