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All Journal International Journal of Recent Technology and Applied Science (IJORTAS)
Alabbas Hussein Saeed
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Explaining Cholesterol-Related Coronary Artery Disease Risk Using Machine Learning and SHAP Eka Pandu Cynthia; Suzani Mohamad Samuri; Wang Shir Li; Alabbas Hussein Saeed; Inggih Permana; Febi Yanto
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 1: March 2026
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0801.920

Abstract

Coronary Artery Disease (CAD) remains a leading cause of global mortality, with dyslipidemia recognized as a major modifiable risk factor. This study investigates the relationship between serum lipid parameters and CAD using the Z-Alizadeh Sani clinical dataset comprising 303 patients with 55 clinical, biochemical, and electrocardiographic attributes. Logistic Regression (LR) and Random Forest (RF) models were developed to predict CAD status, supported by a standardized preprocessing pipeline, multi-split train–test evaluation (70/30, 80/20, 90/10), and performance assessment using Accuracy, Precision, Recall, F1-Score, and AUC-ROC. SHapley Additive exPlanations (SHAP) were employed to enhance model interpretability and quantify the contribution of lipid-related and clinical features to individual predictions. The RF model consistently outperformed LR across all split configurations, achieving a maximum AUC of 0.96, while LR attained an AUC of 0.90. SHAP analysis revealed that total cholesterol (CHOL) and low-density lipoprotein (LDL) were strong positive predictors of CAD, whereas high-density lipoprotein (HDL) exhibited a protective effect, in line with established cardiovascular pathophysiology. These findings demonstrate that integrating explainable machine learning with routine clinical lipid profiles can provide accurate and transparent decision support for early CAD risk stratification.
Co-Authors Eka Pandu Cynthia Febi Yanto Inggih Permana Suzani Mohamad Samuri Wang Shir Li