Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries
Article
Alizadehsani, Roohallah, Roshanzamir, Mohamad, Abdar, Moloud, Beykikhoshk, Adham, Khosravi, Abbas, Nahavandi, Saeid, Plawiak, Pawel, Tan, Ru San and Acharya, Rajendra. 2022. "Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries." Expert Systems: the journal of knowledge engineering. 39 (7). https://doi.org/10.1111/exsy.12573
Article Title | Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries |
---|---|
ERA Journal ID | 17851 |
Article Category | Article |
Authors | Alizadehsani, Roohallah, Roshanzamir, Mohamad, Abdar, Moloud, Beykikhoshk, Adham, Khosravi, Abbas, Nahavandi, Saeid, Plawiak, Pawel, Tan, Ru San and Acharya, Rajendra |
Journal Title | Expert Systems: the journal of knowledge engineering |
Journal Citation | 39 (7) |
Article Number | e12573 |
Number of Pages | 17 |
Year | 2022 |
Publisher | John Wiley & Sons |
Place of Publication | United Kingdom |
ISSN | 0266-4720 |
1468-0394 | |
Digital Object Identifier (DOI) | https://doi.org/10.1111/exsy.12573 |
Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1111/exsy.12573 |
Abstract | Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries. |
Keywords | coronary artery disease; discretization; feature selection; machine learning; uncertainty |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Deakin University |
Azad University, Iran | |
Cracow University of Technology, Poland | |
Polish Academy of Sciences, Poland | |
National Heart Centre, Singapore | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
Permalink -
https://research.usq.edu.au/item/z1v51/hybrid-genetic-discretized-algorithm-to-handle-data-uncertainty-in-diagnosing-stenosis-of-coronary-arteries
45
total views0
total downloads1
views this month0
downloads this month