Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Srinivasaiah, Raghavendra; Jankatti, Santosh Kumar; Jinachandra, Niranjana Shravanabelagola; Lamani, Manjunath Ramanna; Channegowda, Ravikumar Hodikehosahally Title A name given to the resource Early CKD Prediction Using Ensemble and Basic Machine Learning Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Online and Biomedical Engineering;Volume;22;Issue;5;pp.171-184 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3991/ijoe.v22i05.58661" target="_blank" rel="noreferrer noopener">https://doi.org/10.3991/ijoe.v22i05.58661</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038771026?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038771026?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Srinivasaiah R., CHRIST University, Bangalore, India; Jankatti S.K., Dayananda Sagar University, Bangalore, India; Jinachandra N.S., CHRIST University, Bangalore, India; Lamani M.R., Moodlakatte Institute of Technology, Kundapura, India; Channegowda R.H., Dayananda Sagar Academy of Technology and Management, Bengaluru, India Description An account of the resource Chronic kidney disease (CKD) is a progressive illness that often remains undiagnosed until advanced stages and represents a significant global health burden. Proper and timely diagnosis of CKD can significantly improve patient prognosis and reduce treatment costs. This study evaluates several machine learning (ML) models, including support vector machine (SVM), random forest (RF), gradient boosting (GB), Nae Bayes (NB), AdaBoost, and a multilayer perceptron (MLP) neural network. Additionally, it proposes a stacking ensemble model combining RF and GB for accurate CKD prediction using a publicly available Kaggle dataset. Missing value handling and feature normalisation are performed during data preprocessing, and model performance is evaluated using an 80:20 traintest split with metrics such as the area under the curve (AUC), classification accuracy (CA), F1-score, precision, recall, and Matthews Correlation Coefficient (MCC). Experimental results indicate that RF and GB achieve the strongest individual performance, while the proposed stacking ensemble attains the highest CA of 99.4%. These findings highlight the potential of artificial intelligence (AI)-driven predictive models to support proactive CKD diagnosis and enhance clinical decision-making in healthcare systems. 2026 by the authors of this article. Published under CC-BY. Subject The topic of the resource chronic kidney disease (CKD); ensemble learning; gradient boosting (GB); machine learning (ML); prediction Publisher An entity responsible for making the resource available International Federation of Engineering Education Societies (IFEES) Relation A related resource ISSN: 26268493; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online