Hybrid genetic‐discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries
Authors:
- Roohallah Alizadehsani,
- Mohamad Roshanzamir,
- Moloud Abdar,
- Adham Beykikhoshk,
- Abbas Khosravi,
- Saeid Nahavandi,
- Pawel Plawiak,
- Ru San Tan,
- U Rajendra Acharya
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.
- Record ID
- CUTbc243c9c33f0490b816f4c6b02dd7df3
- Publication categories
- ;
- Author
- Journal series
- Expert Systems, ISSN 0266-4720, e-ISSN 1468-0394
- Issue year
- 2022
- Vol
- 39
- No
- 7
- Pages
- [1-17]
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 14-16; Bibliografia (liczba pozycji) - 53; Oznaczenie streszczenia - Abstr.; Data udostępnienia on-line - 2020-06-14; Numeracja w czasopiśmie - Vol. 39, Iss. 7, Spec. Iss.
- Substantive notes
- Special Issue: Recent advances in Deep Learning, Biometrics, Health Informatics and Data Science
- Keywords in English
- coronary artery disease, discretization, feature selection, machine learning, uncertainty
- ASJC Classification
- ; ; ;
- DOI
- DOI:10.1111/exsy.12573 Opening in a new tab
- URL
- https://onlinelibrary.wiley.com/doi/full/10.1111/exsy.12573 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 70
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 32
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTbc243c9c33f0490b816f4c6b02dd7df3/
- URN
urn:pkr-prod:CUTbc243c9c33f0490b816f4c6b02dd7df3
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.