Breast cancer is becoming a global epidemic, affecting predominantly women. As a result, the number of people diagnosed with breast cancer is increasing every day. As a result, it is critical to have certain early detection methods in place that can assist patients in recognizing this condition at an early stage. Therefore, they might begin taking their medication to prevent the sickness from killing them. Different prediction approaches for early diagnosis of such diseases have been created in the machine learning fields. Those algorithms employ a variety of computational classifiers and claim to achieve satisfactory results in a few areas. However, no research was reached to determine which computationally sophisticated approach is more effective in detecting breast cancer. As a result, it is necessary to select the most effective strategy from the available options. This paper makes a contribution to the performance evaluation of 12 alternative classification strategies on datasets of breast cancer. The right explanations for the classifiers' dominance were investigated.