title: A quantifier-based fuzzy classification system for breast cancer patients.
abstract: OBJECTIVE
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: School of Computer Science, Advanced Data Analysis Centre, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK. daniele.soria@nottingham.ac.uk
authors.forename: Daniele
authors.initials: D
authors.lastname: Soria
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Garibaldi
authors.forename: Andrew R
authors.initials: AR
authors.lastname: Green
authors.forename: Desmond G
authors.initials: DG
authors.lastname: Powe
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Nolan
authors.forename: Christophe
authors.initials: C
authors.lastname: Lemetre
authors.forename: Graham R
authors.initials: GR
authors.lastname: Ball
authors.forename: Ian O
authors.initials: IO
authors.lastname: Ellis
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000737:chemistry* / Q000145:classification* / Q000175:diagnosis / Q000628:therapy; D003936:Diagnosis, Computer-Assisted; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007150:Immunohistochemistry; D010363:Pattern Recognition, Automated; D010641:Phenotype; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results
chemical_list: D014408:Biomarkers, Tumor
keywords: Breast cancer; Fuzzy rules; Linguistic ruleset; Rule-based classification