Roohallah Alizadehsani1, Mohammad Javad Hosseini2, Abbas Khosravi3, Fahime Khozeimeh4, Mohamad Roshanzamir5, Nizal Sarrafzadegan6, Saeid Nahavandi1. 1. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia. 2. Department of Computer Science and Engineering, University of Washington, Seattle, United States. 3. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia. Electronic address: abbas.khosravi@deakin.edu.au. 4. Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. 5. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran. 6. Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences,Isfahan,Iran & Faculty of Medicine, SPPH, University of British Columbia, Vancouver,BC, Canada.
Abstract
BACKGROUND AND OBJECTIVE: Cardiovascular diseases are an extremely widespread sickness and account for 17 million deaths in the world per annum. Coronary artery disease (CAD) is one of such diseases with an annual mortality rate of about 7 million. Thus, early diagnosis of CAD is of vital importance. Angiography is currently the modality of choice for the detection of CAD. However, its complications and costs have prompted researchers to seek alternative methods via machine learning algorithms. METHODS: The present study proposes a novel machine learning algorithm. The proposed algorithm uses three classifiers for detection of the stenosis of three coronary arteries, i.e., left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) to get higher accuracy for CAD diagnosis. RESULTS: This method was applied on the extension of Z-Alizadeh Sani dataset which contains demographic, examination, ECG, and laboratory and echo data of 500 patients. This method achieves an accuracy, sensitivity and specificity rates of 96.40%, 100% and 88.1%, respectively for the detection of CAD. To our knowledge, such high rates of accuracy and sensitivity have not been attained elsewhere before. CONCLUSION: This new algorithm reliably distinguishes those with normal coronary arteries from those with CAD which may obviate the need for angiography in the normal group.
BACKGROUND AND OBJECTIVE:Cardiovascular diseases are an extremely widespread sickness and account for 17 million deaths in the world per annum. Coronary artery disease (CAD) is one of such diseases with an annual mortality rate of about 7 million. Thus, early diagnosis of CAD is of vital importance. Angiography is currently the modality of choice for the detection of CAD. However, its complications and costs have prompted researchers to seek alternative methods via machine learning algorithms. METHODS: The present study proposes a novel machine learning algorithm. The proposed algorithm uses three classifiers for detection of the stenosis of three coronary arteries, i.e., left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) to get higher accuracy for CAD diagnosis. RESULTS: This method was applied on the extension of Z-Alizadeh Sani dataset which contains demographic, examination, ECG, and laboratory and echo data of 500 patients. This method achieves an accuracy, sensitivity and specificity rates of 96.40%, 100% and 88.1%, respectively for the detection of CAD. To our knowledge, such high rates of accuracy and sensitivity have not been attained elsewhere before. CONCLUSION: This new algorithm reliably distinguishes those with normal coronary arteries from those with CAD which may obviate the need for angiography in the normal group.
Authors: Jose Ignacio Melero-Alegria; Manuel Cascon; Alfonso Romero; Pedro Pablo Vara; Manuel Barreiro-Perez; Victor Vicente-Palacios; Fernando Perez-Escanilla; Jesus Hernandez-Hernandez; Beatriz Garde; Sara Cascon; Ana Martin-Garcia; Elena Diaz-Pelaez; Jose Maria de Dios; Aitor Uribarri; Javier Jimenez-Candil; Ignacio Cruz-Gonzalez; Baltasara Blazquez; Jose Manuel Hernandez; Clara Sanchez-Pablo; Inmaculada Santolino; Maria Concepcion Ledesma; Paz Muriel; P Ignacio Dorado-Diaz; Pedro L Sanchez Journal: BMJ Open Date: 2019-02-13 Impact factor: 2.692
Authors: R Alizadehsani; M Roshanzamir; M Abdar; A Beykikhoshk; A Khosravi; M Panahiazar; A Koohestani; F Khozeimeh; S Nahavandi; N Sarrafzadegan Journal: Sci Data Date: 2019-10-23 Impact factor: 6.444
Authors: Javad Hassannataj Joloudari; Faezeh Azizi; Mohammad Ali Nematollahi; Roohallah Alizadehsani; Edris Hassannatajjeloudari; Issa Nodehi; Amir Mosavi Journal: Front Cardiovasc Med Date: 2022-02-04