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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