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
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
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