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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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Official URL: http://dx.doi.org/10.1016/j.bspc.2017.01.012

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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
Subjects: Q Science > Q Science (General)
R Medicine > RD Surgery
Divisions: Faculty of Science > Engineering
Faculty of Medicine > Warwick Medical School
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
Dates:
DateEvent
July 2019Published
9 February 2017Available
24 January 2017Accepted
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
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/K02504X/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
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