Breast Cancer Diagnosis Using a Machine Learning Model and Swarm Intelligence Approach

The features selection for machine learning models requires careful consideration. A good selection of features can enable machine learning models to better identify patterns in data and make more accurate predictions. Also, relevant features in the data have an impact on the accuracy of the model and lengthen the training process. In this study, the suggested feature selection strategy is implemented using pigeon inspired optimizer (PIO). The PIO is a continuous swarm intelligent algorithm. The machine learning models were trained and tested using the proposed PIO optimizer in the context of medical data. Both the training and testing steps use the Wisconsin breast cancer dataset. The best results are generated by the Random Forest model, which has accuracy, F-score, recall, and precision values of 97.2%, 97.3%, 97.3%, and 97.3%, respectively. It was concluded that the selected features perform better for classification than the original high-dimensional features, both in terms of accuracy and the F-score. As a consequence of this, the proposed approach can be utilized to better categorize breast cancer data.