Machine learning has achieved wide success in detecting and identifying cancer diseases, especially those based on medical images. To increase the performance of models, machine learning techniques are combined with other techniques in the processing stage. In this research, the ABC algorithm was used in the feature identification process to help the algorithm choose the optimal solution more accurately. In the classification stage, three different algorithms were used that have wide uses in secondary classification, namely Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR). The medical images used are CT images of breast cancer collected in the form of a standard dataset of 570 known as WDBC dataset. The proposed model achieved classification accuracy for the Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) algorithms: 92.98%, 61.40%, and 94.74%, respectively, in the testing stage.