Breast Cancer Prediction Using Chemical Reaction Optimization and Classifier

Nowadays, breast cancer is one of the most ordinary cancers for women around the world. When breast cells grow and divide in an unrestrained way than forming a mass of tissue that call a tumor and then happen breast cancer. Many researchists have applied many applications to diagnose breast cancer. In this paper, a population-based metaheuristic named chemical reaction optimization (CRO) has been used to optimize the number of features. We have applied metaheuristic algorithms along with machine learning methods to predict breast cancer. From the experimental results, it can be observed that the SVM classifier gives the highest accuracy and f1-score among four classifiers (SVM, XGBoost, random forest, and decision tree). The result of the comparison showed that both the f1-score and accuracy are better than the related methods. To find out the best results on the detection of breast cancer using chemical reaction optimization (CRO) and a minimal number of features is the main target of our paper. We use SVM, decision tree, XGBoost, and random forest as the classifiers. For the experiment, the UCI machine learning dataset has been used. We have tried to find the best results in terms of measurement metrics using the proper technique.