Supervised learning and anti-learning of colorectal cancer classes and survival rates from cellular biology parameters

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Roadknight, Chris, Aickelin, Uwe, Qiu, Guoping, Scholefield, John and Durrant, Lindy (2012) Supervised learning and anti-learning of colorectal cancer classes and survival rates from cellular biology parameters. In: 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC, 14-17 Oct 2012, Seoul, South Korea.

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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. Attempts are made to learn relationships between attributes (physical andimmunological) and the resulting tumour stage and survival. Results for conventional machine learning approaches can be considered poor, especially for predicting tumour stages for the most important types of cancer. This poor performance is further investigated and compared with a synthetic, dataset based on the

logical exclusive-OR function and it is shown that there is a significant level of “anti-learning” present in all supervised methods used and this can be explained by the highly dimensional, complex and sparsely representative dataset. For predicting the stage of cancer from the immunological attributes,anti-learning approaches outperform a range of popular algorithms

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/711817
Additional Information: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Aickelin, Professor Uwe
Date Deposited: 19 Jul 2013 08:34
Last Modified: 04 May 2020 16:34
URI: https://eprints.nottingham.ac.uk/id/eprint/2069

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