S. Aruna, S.P. Rajagopalan and L.V. Nandakishore, Dr M.G.R University, India

In this paper, we study the performance criterion of machine learning tools in classifying breast cancer. We compare the data mining tools such as Naïve Bayes, Support vector machines, Radial basis neural networks, Decision trees J48 and simple CART. We used both binary and multi class data sets namely WBC, WDBC and Breast tissue from UCI machine learning depositary. The experiments are conducted in WEKA. The aim of this research is to find out the best classifier with respect to accuracy, precision, sensitivity and specificity in detecting breast cancer.

J48, Naïve Bayes, RBF neural networks, Simple Cart, Support vector machines.