Cluster Analysis of Breast Cancer Data Using Modified BP-RBFN

Abstract

According to the American Cancer Society, around 17,62,450 women have breast cancer in the United States. Though there are many computer-aided diagnosis systems for detecting cancer, the chances of survival of the patients are essential for an efficient cancer management system. This chapter proposes a model for analysing the cancer data using a modified back propagation-based radial basis function neural network. The proposed clustering using BPRBFN has four modules: preprocessing, feature selection, feature clustering and cluster validation. Feature selection is performed using a genetic algorithm. Out of 32 features, 10 essential features are selected and proceeded for further clustering. Radial basis function neural network that is learned using a backpropagation algorithm is used for clustering because of its best approximation. The network is trained using six different training algorithms and compared to find the best training algorithm for an optimal clustering. The final results are validated using the mean-square error index and regression fit value. Wisconsin Breast Cancer (Diagnostic) Dataset is used for all simulations throughout the work.