Comparing the Performances of Support Vector Machines and Artificial Neural Networks for Predicting Coronary Artery
Sahar Shariatnia1,2, Abdolhalim Rajabi2,3, Majid Ziaratban4
1Department of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran, mums.ac.ir.
Insights
Support vector machines (SVMs) outperformed artificial neural networks (ANNs) in diagnosing coronary artery disease (CAD) using clinical predictors. This finding offers a more accurate noninvasive method for detecting CAD, a leading cause of death.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Coronary artery disease (CAD) is a significant global health concern, recognized as an inflammatory condition contributing to widespread morbidity and mortality.
- Cardiovascular disease (CVD) broadly represents a major cause of death and disability worldwide.
- Noninvasive diagnostic techniques are crucial for effective CAD detection and management.
Purpose of the Study:
- To compare the diagnostic performance of artificial neural networks (ANNs) and support vector machines (SVMs) for detecting coronary artery disease (CAD).
- To evaluate the efficacy of machine learning models in identifying CAD using noninvasive clinical predictors.
Main Methods:
- A cross-sectional study involving 758 participants (508 with CAD, 250 without).
- Evaluation of two machine learning models: ANNs and SVMs.
- Assessment of diagnostic performance using receiver operating characteristic (ROC) curves, sensitivity, specificity, and accuracy.
Main Results:
- The SVM model achieved a superior area under the ROC curve (AUC) of 0.793 (95% CI: 0.733-0.853) compared to the ANN model's AUC of 0.752 (95% CI: 0.682-0.823).
- A statistically significant difference (p = 0.03) was observed between the models, indicating SVM's enhanced predictive capability.
- The study included a diverse participant group, with 64% males in the CAD cohort and 66.4% females in the non-CAD cohort.
Conclusions:
- Support vector machines (SVMs) demonstrated superior diagnostic performance over artificial neural networks (ANNs) in predicting CAD risk.
- SVMs offer a more effective noninvasive approach for CAD detection utilizing simple clinical predictors.
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.