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A quantifier-based fuzzy classification system for breast cancer patients

Soria, Daniele; Garibaldi, Jonathan M.; Green, Andrew R.; Powe, Desmond G.; Nolan, Christopher C.; Lemetre, Christophe; Ball, Graham R.; Ellis, Ian O.

Authors

Daniele Soria

Jonathan M. Garibaldi

Andrew R. Green

Desmond G. Powe

Christopher C. Nolan

Christophe Lemetre

Graham R. Ball

Ian O. Ellis



Abstract

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.

Journal Article Type Article
Publication Date Jul 1, 2013
Journal Artificial Intelligence in Medicine
Print ISSN 0933-3657
Electronic ISSN 0933-3657
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 58
Issue 3
APA6 Citation Soria, D., Garibaldi, J. M., Green, A. R., Powe, D. G., Nolan, C. C., Lemetre, C., …Ellis, I. O. (2013). A quantifier-based fuzzy classification system for breast cancer patients. Artificial Intelligence in Medicine, 58(3), doi:10.1016/j.artmed.2013.04.006
DOI https://doi.org/10.1016/j.artmed.2013.04.006
Publisher URL http://www.sciencedirect.com/science/article/pii/S0933365713000699
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
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

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf







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