Evaluation of the effect of feature selection and different kernel functions on SVM performance for Breast Cancer diagnosis

ABSTRACT

Introduction: Breast cancer is one of the most common cancers affecting women. In mammography, differentiating a malignant tumor from a benign one is a very tedious task due to their structural similarities. Machine Learning [ML] is a subfield of Artificial Intelligence that can be used as an effective tool to help physicians to make decisions. Support vector machine [SVM] is one of the most common ML techniques that its performance depends on kernel parameters tuning and input features. The aim of this study was to investigate the effect of feature selection and different kernel functions on SVM performance