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

no code implementations • 21 Jul 2016 • Christopher Roadknight, Durga Suryanarayanan, Uwe Aickelin, John Scholefield, Lindy Durrant

The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours.

BIG-bench Machine Learning feature selection +2

Ensemble Learning of Colorectal Cancer Survival Rates

no code implementations • 2 Sep 2014 • Chris Roadknight, Uwe Aickelin, John Scholefield, Lindy Durrant

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours.

Clustering Ensemble Learning +3

Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification

no code implementations • 5 Jul 2013 • Chris Roadknight, Uwe Aickelin, Alex Ladas, Daniele Soria, John Scholefield, Lindy Durrant

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours.

Classification Clustering +2

Supervised Learning and Anti-learning of Colorectal Cancer Classes and Survival Rates from Cellular Biology Parameters

no code implementations • 5 Jul 2013 • Chris Roadknight, Uwe Aickelin, Guoping Qiu, John Scholefield, Lindy Durrant

For predicting the stage of cancer from the immunological attributes, anti-learning approaches outperform a range of popular algorithms.

Tumour Classification