A Review and Experimental Comparison of Machine Learning Models for Chronic Kidney Disease Prediction

A Review and Experimental Comparison of Machine Learning Models for Chronic Kidney Disease Prediction | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 April 2025 V1 Latest version Share on A Review and Experimental Comparison of Machine Learning Models for Chronic Kidney Disease Prediction Authors : Shifat Shahriar Siam 0009-0003-6329-5336 [email protected] , Nusrat Jahan , Afsana Mohammed Mimi , Nishat Ara Noon , Kamrun Nahar Momo , Sumaiya Yusuf , Md Habibullah , and Md. Al Mamun Authors Info & Affiliations https://doi.org/10.22541/au.174521542.22976041/v1 270 views 104 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: In CKD cases, early detection is disparaging to prevent this disease or related adverse renal conditions. Early identification also lessens related morbidity & mortality. Machine Learning provides effective approaches that helps to intensify the test validity, also enables premature intervention. Objective: This study aims to review and compare existing machine learning models used for chronic kidney disease (CKD) prediction and to experimentally evaluate selected algorithms—Logistic Regression, Support Vector Machine, and Random Forest—on a real-world clinical dataset to validate their performance and interpretability. Methods: In first phase literature review on 20 relevant studies was conducted to extract information like key ml algorithms, performance, and features. In the second phase we conducted an experimental comparison using a public CKD dataset on Kagle. After preprocessing the data, the model of ML like SVM, Logistic regression and Random forest were trained and evaluated by accuracy, precision, recall, F1 score, and ROC-AUC as performance metrics. All the analysis were performed using Python 3.11 in Google Colab environment. Results: Literature review showed ML model accuracy ranging 95 to 100% for CKD prediction. In our Experimental comparison, random Forest outperformed others with perfect scores across all metrics (Accuracy, Precision, Recall, F1 Score, ROC-AUC = 1.00). Logistic Regression and SVM also demonstrated strong results, with F1 scores of 0.9899 and 0.9796, respectively Conclusion: The findings confirm that machine learning models—especially ensemble methods like Random Forest—are highly effective for early CKD prediction. The experimental results align with prior literature and highlight clinically relevant features, demonstrating the potential of ML to support diagnostic decision-making in nephrology. Supplementary Material File (exsy_apr25_1652_revised.docx) Download 826.00 KB Information & Authors Information Version history V1 Version 1 21 April 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords chronic kidney disease (ckd) logistic regression machine learning medical diagnosis predictive modeling random forest support vector machine (svm) Authors Affiliations Shifat Shahriar Siam 0009-0003-6329-5336 [email protected] Jahangirnagar University View all articles by this author Nusrat Jahan Jahangirnagar University View all articles by this author Afsana Mohammed Mimi Jahangirnagar University View all articles by this author Nishat Ara Noon Jahangirnagar University View all articles by this author Kamrun Nahar Momo Jahangirnagar University View all articles by this author Sumaiya Yusuf Jahangirnagar University View all articles by this author Md Habibullah Jahangirnagar University View all articles by this author Md. Al Mamun Jahangirnagar University View all articles by this author Metrics & Citations Metrics Article Usage 270 views 104 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Shifat Shahriar Siam, Nusrat Jahan, Afsana Mohammed Mimi, et al. A Review and Experimental Comparison of Machine Learning Models for Chronic Kidney Disease Prediction. Authorea . 21 April 2025. DOI: https://doi.org/10.22541/au.174521542.22976041/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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