Machine learning model to predict heart transplant failure and success using XGBoost algorithm and SMOTE/ENN to balance the dataset.

Developed predictive models to analyze one-year post-transplant composite outcomes (death, graft failure, or re-transplantation) using Cox Proportional Hazards, Logistic Regression, Random Forest, SVM, and Mixed Effects Models, providing critical insights into recipient survival and transplant success.