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

Artificial bee colony (ABC) is one of the swarmintelligence optimization algorithms, inspired by foraging anddance behaviors of real honey bee colonies. This study is aninstance of a hybrid algorithm using ABC together withk-nearest neighbor algorithm on diagnosis of coronary arterydisease employing exercises stress test data. The study dataset iscomposed of 134 healthy and 346 unhealthy totally 480 patients.On the proposed algorithm two centroid vectors are obtainedconcerning one for healthy patients and the other for unhealthypatients utilizing ABC for the training part of the dataset. Then,the test part of the dataset is classified using k-nearest neighboralgorithm. The results obtained by the proposed technique showthat this hybrid algorithm could be used as an alternativeclassifier on diagnosis of coronary artery disease employingexercise stress test data.