Enhancing Chronic Kidney Disease Classification Using Decision Tree And Bootstrap Aggregating: Uci Dataset Study With Improved Accuracy And Auc-Roc

Zuriati Zuriati; Dian Meilantika; Atika Arpan; Rizka Permata; Sriyanto Sriyanto; Mohd. Zaki  Mas'ud

doi:10.52436/1.jutif.2025.6.5.5271

Authors

Zuriati Department of Internet Engineering Technology, Politeknik Negeri Lampung, Indonesia
Dian Meilantika Department of Information Management, Politeknik Negeri Lampung, Indonesia
Atika Arpan Department of Information Management, Politeknik Negeri Lampung, Indonesia
Rizka Permata Department of Information Management, Politeknik Negeri Lampung, Indonesia
Sriyanto Department of Informatics, Institute of Informatics and Business Darmajaya, Indonesia
Mohd. Zaki Mas'ud Faculty of Artificial Intelligence and Cyber Security, Universiti Teknikal Malaysia Melaka, Malaysia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.5.5271

Keywords:

Bootstrap Aggregating, Chronic Kidney Disease, Classification, Decision Tree, Ensemble Learning

Abstract

Chronic Kidney Disease (CKD) is a progressive medical disorder that requires timely and precise identification to avoid permanent impairment of kidney function. However, Decision Tree models, although widely used in clinical applications due to their transparency, ease of implementation, and ability to handle both categorical and numerical data, are prone to overfitting and instability when applied to small or imbalanced datasets. The purpose of this study is to optimize CKD classification by integrating Bootstrap Aggregating (Bagging) with Decision Tree to enhance accuracy and robustness. The methodology involves testing two model variants a standalone Decision Tree and a Bagging-supported Decision Tree using 10-fold cross-validation and evaluating performance with accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Findings reveal that Bagging enhances model accuracy from 0.980 to 0.987, raises precision from 0.976 to 1.000, and improves recall from 0.954 to 0.954, and increases F1-score from 0.965 to 0.976. These results demonstrate that Bagging significantly improves the reliability and generalizability of Decision Tree classifiers, making them more effective for CKD prediction.

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