Medical Treatment Planing on Heart Transplant - CS486 Artificial Intelligence Co-work of Yue Huang and YueXing Luo
Inspired by the Stanford Heart Transplant Data (Crowley & Hu,1977; Kalbfleisch & Prentice, 2002), we propose an decision tree based model to predict the risk of heart transplant surgery. We investigate five different corresponding attributes that may have influence on the success rate of heart transplantation, as well as the survival rate. Our intuition is to provide some suggestions for patients who tend to receive heart transplantation, by analyzing the effects of various related covariates. To be specific, for those who are not able to find a donor heart to process transplantation surgery, we assess the effects of age and prior surgery experience on the survival of patient. For those who found the donator, we predicate the possibility of heart rejection and provide estimation of the relative risk of transplantation by considering covariates on the success rate of heart transplant surgery.
See the report file for full detail implementation