Feature Selection using Hybrid Dragonfly Algorithm in a Heart Disease Predication System
Namariq Ayad Saeed1, Ziyad Tariq Mustafa Al-Ta’i2
1Namariq Ayad Saeed, Department of computer science, University of Diyala/ computer science, Diyala, Iraq.
2Ziyad Tariq Mustafa AL-Ta’i, Department of computer science, University of Diyala/ computer science, Diyala, Iraq.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2862-2867 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8786088619/2019©BEIESP | DOI: 10.35940/ijeat.F8786.088619
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The heart disease considers as one of the fatal disease in many countries. The main reason is due to the approved methods of diagnostic are not available to the ordinary people. Many studies have been done to handle this case with the use of both methods of soft computing and machine learning. In this study, a hybrid binary dragonfly algorithm and mutual information proposed for feature selection, support vector machine and multilayer perceptron employed for classification. The Statlog dataset used for experiments. Out of a total of 270 instances of patient data, 216 employees for the purpose of practicing, 54 of them used for the purpose of examining. Maximum classification accuracy of 94.44% achieved with support vector machine and 92.59% with multilayer perceptron on features selected with binary dragonfly algorithm, whereas with features obtained from mutual information combined with binary dragonfly (MI_BDA) algorithm support vector machine and multilayer perceptron attained an accuracy of 96.29%. The time algorithm takes reduced from 15.4 with binary dragonfly algorithm to 6.95 seconds with MI_BDA.
Keywords: About four key words or phrases in alphabetical order, separated by commas.