Classification of breast cancer microarray data using radial basis function network

Abstract

Breast cancer is the number one killer disease among women worldwide. Although this disease may affect women and men but the rate of incidence and the number of death is high among women compared to men. Early detection of breast cancer will help to increase the chance of survival since the early treatment can be decided for the patients who suffer this disease. The advent of the microarray technology has been applied to the medical area in term of classification of cancer and diseases. By using the microarray, thousands of genes expression can be determined simultaneously. However, this microarray suffers several drawbacks such as high dimensionality and contains irrelevant genes. Therefore, various techniques of feature selection have been developed in order to reduce the dimensionality of the microarray and also to select only the appropriate genes. For this study, the microarray breast cancer data, which is obtained from the Centre for Computational Intelligence will be used in the experiment. The Relief-F algorithm has been chosen as the method of the feature selection. As the comparison, another two methods of feature selection which are Information Gain and Chi-Square will also be used in the experiment. The Radial Basis Function, RBF network will be used as the classifier to distinguish between the cancerous and non-cancerous cells. The accuracy of the classification will be evaluated by using the chosen metric namely Receiver Operating Characteristic, ROC.