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Journal of the Egyptian Mathematical Society
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Volume Volume 26 (2018)
Issue Issue 3
Issue Issue 2
Issue Issue 1
Darwish, A., Sayed, G., Hassanien, A. (2018). META-HEURISTIC OPTIMIZATION ALGORITHMS BASED FEATURE SELECTION FOR CLINICAL BREAST CANCER DIAGNOSIS. Journal of the Egyptian Mathematical Society, 26(2), 321-336. doi: 10.21608/JOMES.2018.2673.1023
Ashraf Darwish; Gehad Sayed; Aboul Hassanien. "META-HEURISTIC OPTIMIZATION ALGORITHMS BASED FEATURE SELECTION FOR CLINICAL BREAST CANCER DIAGNOSIS". Journal of the Egyptian Mathematical Society, 26, 2, 2018, 321-336. doi: 10.21608/JOMES.2018.2673.1023
Darwish, A., Sayed, G., Hassanien, A. (2018). 'META-HEURISTIC OPTIMIZATION ALGORITHMS BASED FEATURE SELECTION FOR CLINICAL BREAST CANCER DIAGNOSIS', Journal of the Egyptian Mathematical Society, 26(2), pp. 321-336. doi: 10.21608/JOMES.2018.2673.1023
Darwish, A., Sayed, G., Hassanien, A. META-HEURISTIC OPTIMIZATION ALGORITHMS BASED FEATURE SELECTION FOR CLINICAL BREAST CANCER DIAGNOSIS. Journal of the Egyptian Mathematical Society, 2018; 26(2): 321-336. doi: 10.21608/JOMES.2018.2673.1023

META-HEURISTIC OPTIMIZATION ALGORITHMS BASED FEATURE SELECTION FOR CLINICAL BREAST CANCER DIAGNOSIS

Article 10, Volume 26, Issue 2, April 2018, Page 321-336  XML PDF (1.37 MB)
Document Type: Original Article
DOI: 10.21608/JOMES.2018.2673.1023
Authors
Ashraf Darwish1; Gehad Sayed2; Aboul Hassanien2
1Faculty of Science, Helwan University, Egypt
2Faculty of Computers and Information, Cairo University, Egypt
Abstract
Breast cancer is the leading cause of cancer death among women in the whole world. Meanwhile, early detection and
accurate diagnosis can increase the chances of making the right decision on a successful treatment process. This article
presents a two-step system that rst uses four di erent swarm algorithms namely; whale optimization algorithm, grey
wolf optimizer, ower pollination algorithm, and moth ame optimization for feature selection purpose. Then, several
classi ers are applied including support vector machines, k-nearest neighbor, and decision tree. The performance of each
algorithm is evaluated using ve di erent aspects; classi cation based measurements, convergence, computational time,
statistical measurements and stability. The obtained results from the proposed algorithms are compared and analyzed
with other algorithms published in breast cancer diagnosis. The experimental using Wisconsin breast cancer diagnosis
and Wisconsin prognosis breast cancer (WPBC) datasets outcomes positively that the proposed system was e ective in
undertaking breast cancer data classi cation and features selection tasks
Keywords
Intelligent Systems; Breast Cancer Diagnosis; Feature Selection; Math Subject Classi cation: 68; 93; 94
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