Breast cancer diagnosis using feature selection techniques

Abstract

This study focuses on feature selection for breast cancer diagnosis. Since the feature selection became a crucial task in machine learning, we will experiment some filter, wrapper approach and embedded approach on Wisconsin breast cancer dataset, which is commonly used by researchers who use machine-learning methods for breast cancer diagnosis. The performance of the feature selection method is evaluated by classification accuracy using two kinds of classifiers SVM and KNN.