Coronary Artery Disease Diagnosis: Ranking the Significant Features Using a Random Trees Model
- Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of sideHeart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.…
| Document Type: | Article |
|---|---|
| Author: | Javad Hassannataj JoloudariORCiD, Edris Hassannataj Joloudari, Hamid SaadatfarORCiD, Mohammad GhasemiGolORCiD, Seyyed Mohammad RazaviORCiD, Amir MosaviORCiD, Narjes NabipourORCiD, Shahaboddin ShamshirbandORCiD, Laszlo Nadai |
| DOI (Cite-Link): | https://doi.org/10.3390/ijerph17030731Cite-Link |
| URN (Cite-Link): | https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200213-40819Cite-Link |
| URL: | https://www.mdpi.com/1660-4601/17/3/731 |
| Parent Title (English): | International Journal of Environmental Research and Public Health, IJERPH |
| Publisher: | MDPI |
| Language: | English |
| Date of Publication (online): | 2020/01/31 |
| Date of first Publication: | 2020/01/23 |
| Release Date: | 2020/02/13 |
| Publishing Institution: | Bauhaus-Universität Weimar |
| Institutes and partner institutions: | Fakultät Bauingenieurwesen / Institut für Strukturmechanik (ISM) |
| Volume: | 2020 |
| Issue: | Volume 17, Issue 3, 731 |
| Pagenumber: | 24 |
| Tag: | Deep learning; Machine learning; big data; coronary artery disease; data science; ensemble model; health informatics; heart disease diagnosis; industry 4.0; predictive model; random forest |
| GND Keyword: | Maschinelles Lernen |
| Dewey Decimal Classification: | 000 Informatik, Informationswissenschaft, allgemeine Werke |
| BKL-Classification: | 54 Informatik |
| Licence (German): |  Creative Commons 4.0 - Namensnennung (CC BY 4.0) |