Abstract

Breast cancer is a chronic disease which has been classified as a cancer type having one of the highest mortality rates. An early and accurate diagnosis of any chronic disease plays an important role and can be lifesav-ing. Numerous research articles state that the role of com-puterized diagnostic tools supporting the decision making in diagnosis of chronic diseases has increased over the past decade. The presented study implements and evaluates three different artificial neural networks in form of supervised radial basis networks (RBN). The performance of the RBN’s in regards to different center selection methods using different clustering algorithm are evaluated with the help of the Wisconsin breast cancer dataset (WBCD) by UCI machine learning repository.