Conference proceeding
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, pp.99-108
12/07/2020
DOI: 10.5121/csit.2020.101609
Abstract
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 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.
Details
- Title: Subtitle
- A predictive model for kidney transplant graft survival using machine learning
- Creators
- Eric S Pahl - University of IowaW. Nick Street - University of IowaHans J Johnson - University of IowaAlan I Reed - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 4th International Conference on Computer Science and Information Technology (COMIT 2020), November 28-29, 2020, Dubai, pp.99-108
- DOI
- 10.5121/csit.2020.101609
- Language
- English
- Date published
- 12/07/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Bus Admin College; Electrical and Computer Engineering; Psychiatry; Accounting; Surgery; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984185464902771
Metrics
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