Screening shows impact on cancer mortality rate by decreasing the number of advanced cancers with poor diagnosis, while cancer treatment works through decreasing the case-fatality rate. The prediction of breast cancer survivability has been a challenging research problem for many researchers. The objective of this research work is to propose a Novel model that can analysis the Breast cancer data and do efficient prediction. The contributions made in this paper are as follows, we collected three different the dataset from UCI Machine Learning repositories. We propose an approach, where a detailed comparison made between feature selection algorithms. Trained the datasets using Decision Tree, Random Forest and Support vector machine (SVM) machine learning algorithms. An attempt made to understand the impact of model selection metric in predicting different classes of Brest cancer. The results indicated that the Random forest is the best predictor wit 0.98 accuracy on the holdout sample, SVM came out to be the second with 0.97 accuracy and the Decision Tree came out with 0.96 to be the worst of the four condition tree with 0.95 accuracy. Finally performed prediction using Neural Network with three hidden layers and measured the efficiency, using Root Mean Square Error (RMSE) along with its variations. © 2018 SERSC Australia.