Evaluation of computationally intelligent techniques for breast cancer diagnosis

Nowadays, breast cancer is a worldwide prevalent disease mostly in females. Consequently, the breast cancer patients are growing rapidly day by day. Therefore, it is quite essential to have some early detection systems which may help patients to know this disease at an early stage. As a result, they can start their medication to curb this fatal disease. In the era of machine learning, various prediction methods have been developed for early diagnosis of this disease. These algorithms use different computational classifiers and also claim good results in some aspects. But, so far, no proper analysis has been done to clarify which computationally intelligent technique is better to detect breast cancer. Therefore, it is required to find the best among the available methods. In this work, the contribution has been made toward the performance evaluation of seven different classification techniques over breast cancer disease datasets. In addition to this, the proper reasons for the superiority of the classifiers have also been explored.