META-HEURISTIC OPTIMIZATION ALGORITHMS BASED FEATURE SELECTION FOR CLINICAL BREAST CANCER DIAGNOSIS
Ashraf Darwish, Gehad Ismail Sayed, Aboul Ella Hassanien · Journal of the Egyptian Mathematical Society · 2018
Breast cancer is the leading cause of cancer death among women in the whole world. Meanwhile, early detection andaccurate diagnosis can increase the chances of making the right decision on a successful treatment process. This articlepresents a two-step system that rst uses four di erent swarm algorithms namely; whale optimization algorithm, greywolf optimizer, ower pollination algorithm, and moth ame optimization for feature selection purpose. Then, severalclassi ers are applied including support vector machines, k-nearest neighbor, and decision tree. The performance of eachalgorithm 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 analyzedwith other algorithms published in breast cancer diagnosis. The experimental using Wisconsin breast cancer diagnosisand Wisconsin prognosis breast cancer (WPBC) datasets outcomes positively that the proposed system was e ective inundertaking breast cancer data classi cation and features selection tasks