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

Christopher Roadknight, Durga Suryanarayanan, Uwe Aickelin, John Scholefield, Lindy Durrant

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015
EditorsGabriella Pasi, James Kwok, Osmar Zaiane, Patrick Gallinari, Eric Gaussier, Longbing Cao
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467382731
DOIs
Publication statusPublished - 2 Dec 2015
Externally publishedYes
EventIEEE International Conference on Data Science and Advanced Analytics, DSAA 2015 - Paris, France
Duration: 19 Oct 201521 Oct 2015

Publication series

NameProceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015

Conference

ConferenceIEEE International Conference on Data Science and Advanced Analytics, DSAA 2015
Country/TerritoryFrance
CityParis
Period19/10/1521/10/15

Keywords

  • Bioinformatics
  • Ensemble
  • Machine Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems and Management
  • Information Systems

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