In the field of medical research and analysis Breast cancer is one of the most important medical problem. In this paper, various factors that can predict breast cancer are analyzed using spiking neural networks and classification algorithms. The theoretical advantage is that posterior probabilities of malignancy can be estimated directly, and coupled with resampling techniques such as the bootstrap; distributions of the probabilities can also be obtained. This makes it easy to diagnosis breast cancer in a patient based on various factors. The issues of model selection, feature selection, and function approximation are discussed with some detail and illustrated with the application to breast cancer diagnosis. The paper also gives a good introduction to application of spiking neural networks and NEST simulator. In this paper, an analysis on how attributes are co related is estimated and analyzed. For predicting the results a good model is selected using feature selection technique.