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 dierent 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 dierent 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 eective in
undertaking breast cancer data classication and features selection tasks