Holo entropy enabled decision tree classifier for breast cancer diagnosis using wisconsin (prognostic) data set

Abstract

The breast cancer diagnostic and prognostic problems are mainly in the scope of the widely discussed classification problems. These problems have attracted many researchers in computational intelligence, data mining, and statistics fields. The objective of these predictions is to handle cases for which cancer has not recurred (censored data) as well as case for which cancer has recurred at a specific time. The proposed study uses Breast Cancer Wisconsin (Prognostic) Data Set for training and testing purpose. It has implemented holo entropy enable decision tree(HDT). The proposed strategy utilizes the training data to train the classifier. It categorizes each instance of breast cancer growth as recurrent or non recurrent. It ascertains the precision of the classifier to decide the exact classifier accuracy. In the present situation where there is continuous increment in the breast cancer cases and the expanding number of death cases the proposed strategy can be a guide in the determination of breast cancer.