Journal of Kermanshah University of Medical Sciences
Peer-reviewed Medical Quarterly
e-ISSN 2588-2570
p-ISSN 2588-2562
Outline
Using machine learning techniques to differentiate acute coronary syndrome
Authors
1Dept. of Medical Informatics, Faculty of Medical Science, Tarbiat Modares University, Tehran, Iran
2Dept. of Biostatistics and Epidemiology, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
*Corresponding Author: Corresponding author: Ali Asghar Safaei, Dept. of Medical Informatics, Faculty of Medical Science, Tarbiat Modares University, Tehran, Iran, Tel: +982182884581 Email: [email protected]
Published online:Feb 27, 2015
Article type:Letter
Received:Sep 02, 2014
Accepted:Jan 20, 2015
How to Cite:Setareh S, Safaei AA, Najafi F. Using machine learning techniques to differentiate acute coronary syndrome. J Kermanshah Univ Med Sci. 2015;18(11):e73997. doi: https://doi.org/10.22110/jkums.v18i11.2023
Abstract
Backgroud: Acute coronary syndrome (ACS) is an unstable and dynamic process that includes unstable angina, ST elevation myocardial infarction, and non-ST elevation myocardial infarction. Despite recent technological advances in early diognosis of ACS, differentiating between different types of coronary diseases in the early hours of admission is controversial. The present study was aimed to accurately differentiate between various coronary events, using machine learning techniques. Such methods, as a subset of artificial intelligence, include algorithms that allow computers to learn and play a major role in treatment decisions.
Methods: 1902 patients diagnosed with ACS and admitted to hospital were selected according to Euro Heart Survey on ACS. Patients were classified based on decision tree J48. Bagging aggregation algorithms was implemented to increase the efficiency of algorithm.
Results: The performance of classifiers was estimated and compared based on their accuracy computed from confusion matrix. The accuracy rates of decision tree and bagging algorithm were calculated to be 91.74% and 92.53%, respectively.
Conclusion: The proposed methods used in this study proved to have the ability to identify various ACS. In addition, using matrix of confusion, an acceptable number of subjects with acute coronary syndrome were identified in each class.
Highlights
References
- 1.The references of this article is available on PDF.
Copyright
© 2015, Journal of Kermanshah University of Medical Sciences. This open-access article is available under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) International License (https://creativecommons.org/licenses/by-nc/4.0/), which allows for the copying and redistribution of the material only for noncommercial purposes, provided that the original work is properly cited.
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