We analyze lung transplant data from the United Network for Organ Sharing (UNOS) program with the aim of developing accurate risk prediction models for mortality within 1 year of lung transplant using data mining techniques. The data used in this study is de-identified and consists of 62 predictor attributes, and 1-year posttranplant survial outcome for patients who underwent lung transplant between the years 2005 and 2009. Our dataset had 5,319 such patient instances. Several data mining classification techniques were used on this data along with various data mining optimizations and validations to build predictive models for the abovementioned outcome. Prediction results were evaluated using c-statistic metric, and the highest c-statistic obtained was 0.68. Further, we also applied feature selection techniques to reduce the number of attributes in the model from 50 to 8, without any degradation in c-statistic. The final model was also found to outperform logistic regression, which is the most commonly used technique in predictive healthcare informatics. We believe that the resulting predictive model on the reduced dataset can be quite useful to integrate in a risk calculator to aid both physicians and patients in risk assessment.