Breast carcinoma is one of the most significant health diseases in the world. Early identification of breast carcinoma
could be beneficial for in time treatment of the disease. This study presents an efficient classification method
for benign and malignant breast cancer. The proposed method employs an optimal feature classification employing
artificial neural network. The proposed architecture has five input nodes, two hidden layers with eight neurons each
and one output node. Five features (cluster thickness, uniformity of {cell size, cell shape}, marginal attachment and
radius of circle enclosing the abnormality) are nominated as input features to the ANN to predict the benign or
malignant breast carcinoma. The network is trained, tested and validated on data bases that comprises of a set of
previously extracted features provided by Wisconsin and Essex Universities. For the established neural networks comparative
analysis is performed to study the optimum parameters required for prime mass classification. The execution
of suggested methodology is estimated using ROC curve. The accuracy rate of developed method is 93.1% or 0.93
with sensitivity of 0.99 and specificity of 0.83 according to the receiver operating characteristic (ROC).