A quantifier-based fuzzy classification system for breast cancer patients

Soria, Daniele and Garibaldi, Jonathan M. and Green, Andrew R. and Powe, Desmond G. and Nolan, Christopher C. and Lemetre, Christophe and Ball, Graham R. and Ellis, Ian O. (2013) A quantifier-based fuzzy classification system for breast cancer patients. Artificial Intelligence in Medicine, 58 (3). pp. 175-184. ISSN 0933-3657

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (762kB) | Preview


Objectives:Recent studies of breast cancer data have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a range of different clustering techniques. Consensus between unsupervised classification algorithms has been successfully used to categorise patients into these specific groups, but often at the expenses of not classifying the whole set. It is known that fuzzy methodologies can provide linguistic based classification rules. The objective of this study was to investigate the use of fuzzy methodologies to create an easy to interpret set of classification rules, capable of placing the large majority of patients into one of the specified groups.

Materials and methods: In this paper, we extend a data-driven fuzzy rule-based system for classification purposes (called ‘fuzzy quantification subsethood-based algorithm’) and combine it with a novel class assignment procedure. The whole approach is then applied to a well characterised breast cancer dataset consisting of ten protein markers for over 1000 patients to refine previously identified groups and to present clinicians with a linguistic ruleset. A range of statistical approaches was used to compare the obtained classes to previously obtained groupings and to assess the proportion of unclassified patients.

Results: A rule set was obtained from the algorithm which features one classification rule per class, using labels of High, Low or Omit for each biomarker, to determine the most appropriate class for each patient. When applied to the whole set of patients, the distribution of the obtained classes had an agreement of 0.9 when assessed using Kendall's Tau with the original reference class distribution. In doing so, only 38 patients out of 1073 remain unclassified, representing a more clinically usable class assignment algorithm.

Conclusion: The fuzzy algorithm provides a simple to interpret, linguistic rule set which classifies over 95% of breast cancer patients into one of seven clinical groups.

Item Type: Article
Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Artificial Intelligence in Medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Artificial Intelligence in Medicine, 58(3), (2013) doi: 10.1016/j.artmed.2013.04.006
Schools/Departments: University of Nottingham UK Campus > Faculty of Medicine and Health Sciences > School of Medicine
University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.artmed.2013.04.006
Depositing User: Soria, Dr Daniele
Date Deposited: 28 Jan 2015 17:13
Last Modified: 13 Sep 2016 18:59
URI: http://eprints.nottingham.ac.uk/id/eprint/28152

Actions (Archive Staff Only)

Edit View Edit View