Comparing the Performances of Support Vector Machines and Artificial Neural Networks for Predicting Coronary Artery Diseases: A Cross‐Sectional Study

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Abstract
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.
Funding Information
  • Golestan University of Medical Sciences (110374)

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