Classification of Artificial Intelligence Based Coronary Artery Stenosis

Abstract

Background: Despite major advances in diagnoses and treatments, cardiovascular disease (CVD) continues to be the leading cause of morbidity and mortality worldwide. To improve and optimize CVD results, AI techniques have the potential to radically revolutionize the way we practice cardiology, especially in imaging and provide with new tools to interpret data and make clinical decisions. Aim: Establishing strategies are necessary to improve the diagnosis and treatment of CVD in the future. Nowadays, artificial intelligence (AI) may have the potential to solve this problem. The application of AI in heart diseases aims to facilitate the detection of radiology patients. Methods: The machine learning algorithms used in this study are K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Naive Bayes, and Decision Tree. In our study, a total of 600 patients, 300 female and 300 male, who were diagnosed with IHD as a result of the findings obtained from the reports of the patients who underwent CAG in the Fırat University Hospital were included in our study. Accuracy, precision, sensitivity, specificity, and F1-score performance values were obtained by the classification. Results: Among the algorithms we have used, KNN had the highest success rate. It was followed by SVM in the second success rate. The success rate of RCA was 83% in KNN, and it was 75% in SVM. While the success rate of LCx in KNN was 76%, it was 68% in SVM. Similarly, the success rate of LAD in KNN was 73%, and it was 71% in SVM. Conclusion: The demand for CAG will be rising in the coming years, owing to an increase in HR. Therefore, new strategies will be sought to reduce the duration of CAG. We consider the application of AI in routine clinical practice. Keywords: Right coronary artery, Left coronary artery, Stenosis, Artificial intelligence