A predictive model for kidney transplant graft survival using machine
learning
- URL: http://arxiv.org/abs/2012.03787v1
- Date: Mon, 7 Dec 2020 15:29:51 GMT
- Title: A predictive model for kidney transplant graft survival using machine
learning
- Authors: Eric S. Pahl, W. Nick Street, Hans J. Johnson, Alan I. Reed
- Abstract summary: 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.
The random forest predicted significantly more successful and longer-surviving transplants than the risk index.
- Score: 1.514049362441354
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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