Diagnosis of Coronary Artery Disease Based on Machine Learning Algorithms Support Vector Machine, Artificial Neural Network, and Random Forest

Abstract

Background: Coronary artery disease (CAD) is known as the most common cardiovascular disease. The development of CAD is influenced by several risk factors. Diagnostic and therapeutic methods of this disease have many and costly side effects. Therefore, researchers are looking for cost-effective and accurate methods to diagnose this disease. Machine learning algorithms can help specialists diagnose the disease early. The aim of this study is to detect CAD using machine learning algorithms. Materials and Methods: In this study, three data mining algorithms support vector machine (SVM), artificial neural network (ANN), and random forest were used to predict CAD using the Isfahan Cohort Study dataset of Isfahan Cardiovascular Research Center. 19 features with 11495 records from this dataset were used for this research. Results: All three algorithms achieved relatively close results. However, the SVM had the highest accuracy compared to the other techniques. The accuracy was calculated as 89.73% for SVM. The ANN algorithm also obtained the high area under the curve, sensitivity and accuracy and provided acceptable performance. Age, sex, Sleep satisfaction, history of stroke, history of palpitations, and history of heart disease were most correlated with target class. Eleven rules were also extracted from this dataset with high confidence and support. Conclusion: In this study, it was shown that machine learning algorithms can be used with high accuracy to detect CAD. Thus, it allows physicians to perform timely preventive treatment in patients with CAD.

Comparing the performances of support vector machines, artificial neural network, and a logistic regression model for predicting coronary artery diseases: a cross‑sectional study

“…The study conducted by Ayatollahi H et al [34], aimed at comparing the performance of SVM and ANN models in predicting CAD, showed that the accuracy of SVM model is higher than that of ANN model. Similarly, the study by Saeedbakhsh S et al [35], which aimed at detecting CAD using machine learning algorithms, showed that the accuracy of SVM was 88.8% and for ANN it was 88.5%. In Kumar R et al [36].…”

“…Our ndings indicate that the sensitivity for predicting CAD is higher for SVM and ANN models compared to the LR model, but SVM and ANN models are equal. In Nusinovici [35] showed that the sensitivity of SVM and ANN models was 0.574 and 0.582, respectively. These results are similar to our ndings and indicate that SVM and ANN models have similar sensitivity indices.…”