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Ensemble learning of colorectal cancer survival rates

Roadknight, Chris; Aickelin, Uwe; Scholefield, John; Durrant, Lindy

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Authors

Chris Roadknight

Uwe Aickelin

John Scholefield

Lindy Durrant



Abstract

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved.

Citation

Roadknight, C., Aickelin, U., Scholefield, J., & Durrant, L. Ensemble learning of colorectal cancer survival rates.

Conference Name IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) 2013
End Date Jul 17, 2013
Deposit Date Sep 30, 2014
Peer Reviewed Peer Reviewed
Keywords Biomedical, Informatics
Public URL https://nottingham-repository.worktribe.com/output/1001349
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6617400
Additional Information Published in: 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications: CIVEMSA 2013: proceedings, July 15-17, 2013, Università degli Studi di Milano, Milan, Italy. Piscataway, NJ : IEEE, 2013. (ISBN: 9781467347013), pp. 82-86 (doi: 10.1109/CIVEMSA.2013.6617400). © IEEE 2013

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