Application of the Binary Logistic Regression Model in Studying Coronary Artery Disease Among Patients Undergoing Catheterization

Authors

Keywords:

Coronary Artery Disease (CAD); Binary Logistic Regression; Risk Factors; Predictive Modeling; ROC Curve Analysis.

Abstract

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide, underscoring the need for reliable methods to identify key risk factors. This study applied binary logistic regression to assess the most significant predictors of CAD among 856 patients who underwent cardiac catheterization at the Benghazi Heart Center between 2020 and 2022. The independent variables examined were age, gender, diabetes, blood clots, smoking, and hypertension. Results from contingency tables and logistic regression analysis revealed that age, male gender, diabetes, and blood clots were statistically significant predictors of CAD (p < 0.05), with diabetes and blood clots having the highest odds ratios (OR = 2.851 and OR = 2.941, respectively). All regression methods (Enter, Forward Stepwise, Backward Stepwise) demonstrated consistent findings, with a correct classification rate of 78.2% and AUC values around 0.727, indicating good model performance. These findings confirm that binary logistic regression is an effective tool for identifying high-risk groups and can support clinical decision-making and preventive healthcare strategies.

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Published

2025-09-25

How to Cite

Alshikhe, W., El- khafifi , F. F., & El-Saeiti, I. (2025). Application of the Binary Logistic Regression Model in Studying Coronary Artery Disease Among Patients Undergoing Catheterization. Libyan International Journal of Natural Sciences, 1(2), 1–8. Retrieved from https://aonsrt.ly/iljs/index.php/iljsen/article/view/27

Issue

Vol. 1 No. 2 (2025): Upcoming Issue

Section

Statistics

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