Ensemble learning of colorectal cancer survival rates

Roadknight, Chris and Aickelin, Uwe and Scholefield, John and Durrant, Lindy (2013) Ensemble learning of colorectal cancer survival rates. In: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) 2013, 15-17 July 2013, Milan, Italy.

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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.

Item Type: Conference or Workshop Item (Paper)
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
Keywords: Biomedical, Informatics
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
University of Nottingham, Malaysia Campus > Faculty of Science > School of Computer Science
University of Nottingham UK Campus > Faculty of Medicine and Health Sciences > School of Medicine
Depositing User: Aickelin, Professor Uwe
Date Deposited: 30 Sep 2014 10:02
Last Modified: 14 Sep 2016 01:21
URI: http://eprints.nottingham.ac.uk/id/eprint/3344

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