Volume 2 Number 1 (Jan. 2013)
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IJCCE 2013 Vol.2(1): 56-59 ISSN: 2010-3743
DOI: 10.7763/IJCCE.2013.V2.136

Diagnosis of Coronary Artery Disease Using Artificial Bee Colony and K-Nearest Neighbor Algorithms

İsmail Babaoğlu, Mustafa Servet Kıran, Erkan Ülker, and Mesut Gündüz

Abstract—Artificial bee colony (ABC) is one of the swarm intelligence optimization algorithms, inspired by foraging and dance behaviors of real honey bee colonies. This study is an instance of a hybrid algorithm using ABC together with k-nearest neighbor algorithm on diagnosis of coronary artery disease employing exercises stress test data. The study dataset is composed of 134 healthy and 346 unhealthy totally 480 patients. On the proposed algorithm two centroid vectors are obtained concerning one for healthy patients and the other for unhealthy patients utilizing ABC for the training part of the dataset. Then, the test part of the dataset is classified using k-nearest neighbor algorithm. The results obtained by the proposed technique show that this hybrid algorithm could be used as an alternative classifier on diagnosis of coronary artery disease employing exercise stress test data.

Index Terms—Coronary artery disease, exercise stress testing, artificial bee colony

The authors are with the Department of Computer Engineering, Selçuk University, Konya, Turkey (e-mail: ibabaoglu@selcuk.edu.tr).

Cite: İsmail Babaoğlu, Mustafa Servet Kıran, Erkan Ülker, and Mesut Gündüz, "Diagnosis of Coronary Artery Disease Using Artificial Bee Colony and K-Nearest Neighbor Algorithms," International Journal of Computer and Communication Engineering vol. 2, no. 1, pp. 56-59 , 2013.

General Information

ISSN: 2010-3743 (Online)
Abbreviated Title: Int. J. Comput. Commun. Eng.
Frequency: Quarterly
Editor-in-Chief: Dr. Maode Ma
Abstracting/ Indexing: EI (INSPEC, IET), Google Scholar, Crossref, EBSCO, ProQuest, and Electronic Journals Library
E-mail: ijcce@iap.org
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