Breast cancer diagnosis improvement using feature selection

Abstract

The objective of this research is to improve the breast cancer diagnosis performance by applying feature selection methods to several classification algorithms. This study uses Winconsin Breast Cancer Dataset. Feature selection methods based on Rough Set and F-score are used for several classification algorithms, which are Sequential Minimal Optimization, Multi-Layer Perceptron, Naive-Bayes, C4. 5, Instance Base Learning, and PART. This study uses 10-fold cross validation as an evaluation method. The results show that feature selection methods can improve diagnosis performance with a smaller number of features.