Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical
datasets include a large variety of data that can lead to confusion during diagnosis. The
Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires
elimination of inappropriate and repeated data from the dataset before final diagnosis. This
can be done using any of the feature selection algorithms available in data mining. Feature
selection is considered as a vital step to increase the classification accuracy. This paper …