  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.
