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.Comment: This work has been published: Pahl ES, Street WN, Johnson HJ, Reed
AI. "A Predictive Model for Kidney Transplant Graft Survival Using Machine
Learning." 4th International Conference on Computer Science and Information
Technology (COMIT 2020), November 28-29, 2020, Dubai, UAE. ISBN:
978-1-925953-30-5. Volume 10, Number 16.10.5121/csit.2020.10160