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All Journal International Journal of Applied Mathematics and Computing.
Br Barus, Eka Valencia
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Computer Science & IT Mathematics

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Identification of Risk Factors for Chronic Kidney Disease Using Binary Logistic Regression Kosasih, Eva; Asmara Santhi, Ni Kadek Wulanda; Febriyanti, Ni Wayan Atik; Br Barus, Eka Valencia; Susilawati, Made
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.222

Abstract

Chronic Kidney Disease (CKD) is a major global health issue that can lead to serious complications and long-term medical care. This study aims to identify key clinical factors associated with CKD status using binary logistic regression analysis. The dataset, obtained from Kaggle, contains 400 patient records with various clinical and demographic attributes. The dependent variable is CKD status (positive or negative), while the independent variables include age, blood pressure, hemoglobin level, urine albumin level, and serum creatinine. Initial analysis involved descriptive statistics and multicollinearity checks, followed by model estimation and evaluation using likelihood ratio and Wald tests. The final model identified four significant predictors: blood pressure, hemoglobin, urine albumin, and serum creatinine. The model achieved a high classification accuracy of 95.50% and an Area Under the ROC Curve (AUC) of 98.78%, indicating excellent predictive performance. These results highlight the importance of these clinical indicators in early CKD detection and support their use in risk assessment models for kidney disease screening Keywords: Chronic Kidney Disease, Binary Logistic Regression, Likelihood Ratio Test, Wald Test, Classification Accuracy
Co-Authors Asmara Santhi, Ni Kadek Wulanda Febriyanti, Ni Wayan Atik Kosasih, Eva Made Susilawati