This project proposes breast cancer identification using Bayesian inference. We evaluated the prognostic value of lymph node ratio (LNR) for the survival of breast cancer patients using Bayesian inference. A prognostic modeling framework was proposed using Bayesian inference to estimate the impact of LNR in breast cancer survival. Based on the proposed model, we then developed a web application for estimating LNR and predicting overall survival. The final survival model with LNR outperformed the other models considered (C-statistic 0.71). Compared to directly measured LNR estimated LNR slightly increased the accuracy of the prognostic model. Model diagnostics and predictive performance confirmed the effectiveness of Bayesian modeling and the prognostic value of the LNR in predicting breast cancer survival. The estimated LNR was found to have a significant predictive value for the overall survival of breast cancer patients. We used Bayesian inference to estimate LNR which was then used to predict overall survival. The models were developed from a large population based cancer registry. We also built a user friendly web application for individual patient survival prognosis. The diagnostic value of the LNR and the effectiveness of the proposed model were evaluated by comparisons with existing prediction models. This project is implemented with MATLAB software.