Conference Proceedings

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

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

Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA’2015) | IEEE | Published : 2015


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 underst..

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University of Melbourne Researchers