An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates

Roadknight, Chris and Suryanarayanan, Durga and Aickelin, Uwe and Scholefield, John and Durrant, Lindy (2015) An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2015), 19-21 Oct 2015, Paris, France.

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Abstract

This paper primarily addresses a dataset relating to cellular, chemical and physical conditions of patients gathered at the time they are operated upon to remove colorectal tumours. This data provides a unique insight into the biochemical and immunological status of patients at the point of tumour removal along with information about tumour classification and post-operative survival. The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it is possible to predict survival rate more accurately using a selection of machine learning techniques applied to subsets of data to gain a deeper understanding of the relationships between a patient’s biochemical markers and survival. We use a range of feature selection and single classification techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients which initially produces less than ideal results. The performance of each model individually is then compared with subsets of the data where agreement is reached for multiple models. This novel method of selective ensembling demonstrates that significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved. Finally we point at a possible method to identify whether a patients prognosis can be accurately predicted or not.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published in: Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics : IEEE/ACM DSAA'2015 : 19-21 Oct 2015, Paris, France. Piscataway, N.J. : IEEE, 2015. ISBN: 978-1-4673-8272-4. pp. 1-8, doi:10.1109/DSAA.2015.7344863 ©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
Keywords: Ensemble, Bioinformatics, Machine Learning
Schools/Departments: University of Nottingham, Ningbo Campus > Faculty of Science and Engineering > Division of Computer Science
University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Depositing User: Aickelin, Professor Uwe
Date Deposited: 17 Jun 2016 14:32
Last Modified: 19 Sep 2016 06:57
URI: http://eprints.nottingham.ac.uk/id/eprint/34117

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