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
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.
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