A Comprehensive Review of Chronic Kidney Disease Using Machine Learning

DOI

10.2991/978-94-6239-727-9_16How to use a DOI?

Keywords

chronic kidney disease; machine learning; classification algorithm; decision tree; explainable ai

Abstract

Chronic Kidney Disease (CKD) is a significant public health issue leading to considerable morbidity and mortality and necessitating early diagnosis for its prevention from advancing to the end stage of renal failure. Recently, machine learning (ML) has also been applied as an effective technique to predict CKD by analyzing complicated correlations in medical data. This paper reviews the recent developments on ML based techniques for CKD prediction including supervised, unsupervised and hybrid methods. The performance of various models like logistic regression, support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting (GB) and deep learning (DL) models is discussed in terms of their accuracy, feature selection methods, data pre-processing methods, interpretability. Clinically important features such as blood pressure, serum creatinine, hemoglobin, albumin and demographic characteristics are particularly focused on, as they are well-established indicators of disease progression and have significant impact on model performance. It also discusses challenges like data imbalance, missing values, and the demand for explainable models to increase clinical trust. Experiments show that ensemble and hybrid learning approaches obtain the best prediction accuracy and robustness. In conclusion, we discuss future avenues and emphasize the need for incorporating data-driven, interpretable ML models into healthcare systems for early and accurate CKD prediction and clinical recommendations.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kaushal Sharma
AU  - Saurabh Shah
AU  - Snehlata Barde
PY  - 2026
DA  - 2026/07/22
TI  - A Comprehensive Review of Chronic Kidney Disease Using Machine Learning
BT  - Proceedings of the International Conference on Sustainable Micro-Nano Materials & Innovative Technology (ICSUMMIT 2026)
PB  - Atlantis Press
SP  - 217
EP  - 226
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6239-727-9_16
DO  - 10.2991/978-94-6239-727-9_16
ID  - Sharma2026
ER  -