Title: A deep analysis of chronic kidney disease for early detection using machine learning classifiers

Authors: Saurabh Pal

Addresses: Department of Computer Applications, VBS Purvanchal University, Jaunpur, India

Abstract: All patients who have suffered by chronic kidney disease (CKD) are not easily identified due to symptoms on early stage. The main objective of this research paper is to develop a CKD prediction model, which can give better accuracy as compared to other studies. In this paper, we have organised CKD datasets from UCI machine learning repository. Most significant CKD features are eliminated by recursive feature elimination techniques. We have trained base classifiers RF, K-NN, MLP and SVM on 70% disease dataset and test on 30% dataset. Bagging and voting ensemble methods are used to enhance the prediction model. The proposed bagging ensemble model outperformed the other classifiers by achieving 97% accuracy.

Keywords: chronic kidney disease; CKD; recursive feature elimination; RFE; random forest; K-nearest neighbour; multilayer perception; support vector machine; voting and bagging ensemble classifier.

DOI: 10.1504/IJMEI.2025.145849

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.3, pp.279 - 291

Received: 21 Apr 2022
Accepted: 10 Sep 2022
Published online: 30 Apr 2025 *

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