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Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation
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Shaikhina, Torgyn, Lowe, David Philip, Daga, Sunil, Briggs, David, Higgins, Rob and Khovanova, N. A. (2019) Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomedical Signal Processing and Control, 52 . pp. 456-462. doi:10.1016/j.bspc.2017.01.012
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WRAP_2017_Decision_trees_Torgyn.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1542Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.bspc.2017.01.012
Abstract
Clinical datasets are commonly limited in size, thus restraining applications of Machine Learning (ML)techniques for predictive modelling in clinical research and organ transplantation. We explored thepotential of Decision Tree (DT) and Random Forest (RF) classification models, in the context of smalldataset of 80 samples, for outcome prediction in high-risk kidney transplantation. The DT and RF modelsidentified the key risk factors associated with acute rejection: the levels of the donor specific IgG anti-bodies, the levels of IgG4 subclass and the number of human leucocyte antigen mismatches betweenthe donor and recipient. Furthermore, the DT model determined dangerous levels of donor-specific IgGsubclass antibodies, thus demonstrating the potential of discovering new properties in the data whentraditional statistical tools are unable to capture them. The DT and RF classifiers developed in this workpredicted early transplant rejection with accuracy of 85%, thus offering an accurate decision supporttool for doctors tasked with predicting outcomes of kidney transplantation in advance of the clinicalintervention.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > Q Science (General) R Medicine > RD Surgery |
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| Divisions: | Faculty of Science > Engineering Faculty of Medicine > Warwick Medical School |
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| Library of Congress Subject Headings (LCSH): | Kidneys -- Transplantation, Graft rejection -- Prevention, Machine learning , Decision trees | ||||||||
| Journal or Publication Title: | Biomedical Signal Processing and Control | ||||||||
| Publisher: | Elsevier BV | ||||||||
| ISSN: | 1746-8094 | ||||||||
| Official Date: | July 2019 | ||||||||
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| Volume: | 52 | ||||||||
| Page Range: | pp. 456-462 | ||||||||
| DOI: | 10.1016/j.bspc.2017.01.012 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
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