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

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

Authors

Chris Roadknight

Durga Suryanarayanan

Uwe Aickelin

John Scholefield

LINDY DURRANT lindy.durrant@nottingham.ac.uk
Professor of Cancer Immunotherapy



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.

Peer Reviewed Peer Reviewed
APA6 Citation Roadknight, C., Suryanarayanan, D., Aickelin, U., Scholefield, J., & Durrant, L. (in press). An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates
Keywords Ensemble, Bioinformatics, Machine Learning
Publisher URL http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7344863
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
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.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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