Volume 10, Issue 4 (June 2016 2016)                   Qom Univ Med Sci J 2016, 10(4): 22-35 | Back to browse issues page

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Shafiee H, Ebrahimi M. Accurate Prediction of Coronary Artery Disease Using Bioinformatics Algorithms. Qom Univ Med Sci J 2016; 10 (4) :22-35
URL: http://journal.muq.ac.ir/article-1-926-en.html
1- University of Qom
2- Faculty of Basic Sciences, University of Qom, Qom , mansour@future.edu
Abstract:   (7271 Views)

Background and Objectives: Cardiovascular disease is one of the main causes of death in developed and Third World countries. According to the statement of the World Health Organization, it is predicted that death due to heart disease will rise to 23 million by 2030. According to the latest statistics reported by Iran’s Minister of health, 3.39% of all deaths are attributed to cardiovascular diseases and 19.5% are related to myocardial infarction. The aim of this study was to predict coronary artery disease using data mining algorithms.

Methods: In this study, various bioinformatics algorithms, such as decision trees, neural networks, support vector machines, clustering, etc., were used to predict coronary heart disease. The data used in this study was taken from several valid databases (including 14 data).

Results: In this research, data mining techniques can be effectively used to diagnose different diseases, including coronary artery disease. Also, for the first time, a prediction system based on support vector machine with the best possible accuracy was introduced.

Conclusion: The results showed that among the features, thallium scan variable is the most important feature in the diagnosis of heart disease. Designation of machine prediction models, such as support vector machine learning algorithm can differentiate between sick and healthy individuals with 100% accuracy.

Full-Text [PDF 574 kb]   (4545 Downloads)    
Type of Study: Original Article |
Received: 2016/06/15 | Accepted: 2016/06/15 | Published: 2016/06/15

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