Selecting Features Subsets Based on Support Vector Machine-Recursive Features Elimination and One Dimensional-Naïve Bayes Classifier using Support Vector Machines for Classification of Prostate and Breast Cancer

Abstract

Deaths caused by cancer are expected to continue to increase, especially for prostate cancer and breast cancer. Both diseases are the most common types of cancer for men and women in the world. The number of deaths can be reduced by the early detection of using machine learning. One of them is the classification of prostate cancer and breast cancer data. Cancer data used has a variety of features, but not all features are essential features. In this study, we used Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and One-Dimensional Naïve Bayes Classifier (1-DBC) as feature selection methods. In both methods, it will get a ranking for each feature. The use of these two methods in the classification of prostate cancer and breast cancer data results in a high level of evaluation. Both of these methods can produce an accuracy rate of 95.61%, the precision of 100%, and recall of 93.61%. In additional evaluation, SVM-RFE has lower running time than 1-DBC.