Cardiology research and practice · 2026
Comparing the Performances of Support Vector Machines and Artificial Neural Networks for Predicting Coronary Artery Diseases: A Cross-Sectional Study.
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Shariatnia Sahar, Rajabi Abdolhalim, Ziaratban Majid, Salehi Aref, Vakili Mohammadali
Аннотация
BACKGROUND: Coronary artery disease (CAD) is recognized as an inflammatory condition and remains a leading cause of morbidity and mortality worldwide. Cardiovascular disease (CVD), more broadly, is a major contributor to global death and disability. This study aimed to compare the diagnostic performance of various noninvasive techniques for detecting CAD.
METHODS: A cross-sectional study was conducted involving 758 participants, including 508 patients diagnosed with CAD and 250 without the disease. The diagnostic performance of two machine learning models-artificial neural networks (ANNs) and support vector machines (SVMs)-was evaluated. The classification models were assessed using receiver operating characteristic (ROC) curves, sensitivity, specificity, and overall accuracy.
RESULTS: The study included a total of 758 participants. Among them, 250 individuals (33.6% male and 66.4% female) were diagnosed as non-CAD cases, while 508 participants (64% male, 36% female) were identified as having CAD. The area under the ROC curve (AUC) for CAD prediction was 0.752 (95% CI: 0.682-0.823) using the ANN model and 0.793 (95% CI: 0.733-0.853) using the SVM model. A statistically significant difference was observed between the performance of the two models in predicting CAD (p = 0.03), with the SVM model demonstrating superior predictive performance (AUC = 0.793, 95% CI: 0.733-0.853).
CONCLUSIONS: SVMs demonstrated superior performance compared to ANNs in predicting the risk of CAD using simple clinical predictors.
METHODS: A cross-sectional study was conducted involving 758 participants, including 508 patients diagnosed with CAD and 250 without the disease. The diagnostic performance of two machine learning models-artificial neural networks (ANNs) and support vector machines (SVMs)-was evaluated. The classification models were assessed using receiver operating characteristic (ROC) curves, sensitivity, specificity, and overall accuracy.
RESULTS: The study included a total of 758 participants. Among them, 250 individuals (33.6% male and 66.4% female) were diagnosed as non-CAD cases, while 508 participants (64% male, 36% female) were identified as having CAD. The area under the ROC curve (AUC) for CAD prediction was 0.752 (95% CI: 0.682-0.823) using the ANN model and 0.793 (95% CI: 0.733-0.853) using the SVM model. A statistically significant difference was observed between the performance of the two models in predicting CAD (p = 0.03), with the SVM model demonstrating superior predictive performance (AUC = 0.793, 95% CI: 0.733-0.853).
CONCLUSIONS: SVMs demonstrated superior performance compared to ANNs in predicting the risk of CAD using simple clinical predictors.