Predictive Modeling for Organ Transplantation Outcomes

The prevalence of end-stage renal disease in the U.S. Has grown significantly, and continues to do so. Organ transplantation generally has better overall patient outcomes than dialysis. But there is a significant shortage of kidneys. This shortage is exacerbated by the need for kidneys for patients with dual organ transplantations. So the kidney allocation problem is a significant challenge. Predictive analytics based clinical decision support systems need to be developed to help physicians make difficult organ allocation decisions. In this paper, we investigate two different classifiers to predict the outcomes of kidney-liver dual transplant patients. The models were evaluated on the basis of overall accuracy, root mean squared error and Area under ROC. UNOS data was used to develop the models.