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

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

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

Citation

Roadknight, C., Aickelin, U., Qiu, G., Scholefield, J., & Durrant, L. Supervised learning and anti-learning of colorectal cancer classes and survival rates from cellular biology parameters. Presented at 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC

• Conference Name: 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC
• End Date: Oct 17, 2012
• Deposit Date: Jul 19, 2013
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/711817
• Publisher URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6377825
• 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.