  Kidney transplantation is the best treatment for end-stage renal failure
patients. The predominant method used for kidney quality assessment is the Cox
regression-based, kidney donor risk index. A machine learning method may
provide improved prediction of transplant outcomes and help decision-making. A
popular tree-based machine learning method, random forest, was trained and
evaluated with the same data originally used to develop the risk index (70,242
observations from 1995-2005). The random forest successfully predicted an
additional 2,148 transplants than the risk index with equal type II error rates
of 10%. Predicted results were analyzed with follow-up survival outcomes up to
240 months after transplant using Kaplan-Meier analysis and confirmed that the
random forest performed significantly better than the risk index (p<0.05). The
random forest predicted significantly more successful and longer-surviving
transplants than the risk index. Random forests and other machine learning
models may improve transplant decisions.
