Early CKD Prediction Using Ensemble and Basic Machine Learning Models
DOI:
https://doi.org/10.3991/ijoe.v22i05.58661Keywords:
Chronic Kidney Disease, Machine Learning, Prediction, Gradient Boosting, Cross ValidationAbstract
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), Naïve 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 train–test 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.
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Copyright (c) 2026 Raghavendra Srinivasaiah, Santosh Kumar Jankatti, Niranjana Shravanabelagola Jinachandra, Manjunath Ramanna Lamani, Ravikumar Hodikehosahally Channegowda
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