An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates
This work addresses a specific problem in oncology by improving survival prediction for colorectal cancer patients, though it is incremental as it builds on existing machine learning techniques with a new ensembling approach.
The paper tackled predicting 5-year survival rates for colorectal cancer patients with TNM stage 2 and 3 tumors using biochemical and immunological data, achieving significant accuracy improvements through a novel selective ensembling method that leverages model agreement on subsets of data.
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.