Abstract: a medical test that provides diagnostic relevant information of the heart activity is obtained by means of an ElectroCardioGram (ECG). Many heart diseases can be found by analyzing ECG because this method with moral performance is very helpful for shaping human heart status. Support Vector Machines (SVM) has been widely applied in classification. In this paper we present the SVM parameter optimization approach using novel metaheuristic for evolutionary optimization algorithms is Cat Swarm Optimization Algorithm (CSOA). The results obtained assess the feasibility of new hybrid (SVMs -CSOA) architecture and demonstrate an improvement in terms of accuracy.

Assist. Prof. Majida Ali Abed and Assist. Prof. Dr. Hamid Ali Abed Alasad, “New Hybrid (SVMs -CSOA) Architecture for classifying Electrocardiograms Signals” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 4(5), 2015. http://dx.doi.org/10.14569/IJARAI.2015.040505

@article{Abed2015,
title = {New Hybrid (SVMs -CSOA) Architecture for classifying Electrocardiograms Signals},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2015.040505},
url = {http://dx.doi.org/10.14569/IJARAI.2015.040505},
year = {2015},
publisher = {The Science and Information Organization},
volume = {4},
number = {5},
author = {Assist. Prof. Majida Ali Abed and Assist. Prof. Dr. Hamid Ali Abed Alasad}
}