Document Type : Original Article
Authors
1
Department of Computer Engineering, Islamic Azad University, Shiraz Branch, Shiraz, Iran
2
Assistant Professor, Department of Computer Engineering, Islamic Azad University, Shiraz Branch, Shiraz, Iran
Abstract
Cardiovascular diseases are the leading cause of death in the world. In this regard, the rapid and timely diagnosis of heart diseases and the prediction of certain risk events associated with the cardiovascular system are among the top priorities of researchers. Due to the risks of invasive diagnostic methods in coronary artery disease, such as angiography, providing a suitable and non-invasive method for timely diagnosis, increasing accuracy, reducing errors in decision-making, reducing treatment costs and improving the quality of services provided by physicians has been the main goal of this research. In the implementation of this practical research, the Cleveland medical data set, consisting of 270 samples with 76 features, and Z-AlizadehSani data set, consisting of 303 samples with 54 features, available in the UCI standard data repository, were used. Initially, preprocessing and feature selection, followed by modeling, data processing and analysis was performed by examining the effect of disease parameters on coronary artery stiffness using a combination of machine learning algorithms. The proposed system, based on accuracy, sensitivity, specificity, and AUC indices, was able to achieve the best performance with the lowest error compared to similar research. Based on the results obtained, the proposed model can prevent potential adverse effects and damages of some invasive procedures such as angiography in patients who do not need it. Moreover, the system can help physicians triage patients who definitely need these diagnostic procedures in order to receive timely treatment with the highest precision.
Keywords
)