Artif Intell Med - A quantifier-based fuzzy classification system for breast cancer patients.

Tópicos

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Resumo

JECTIVES: 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.

Resumo Limpo

jectiv recent studi breast cancer data identifi seven distinct clinic phenotyp group use immunohistochem analysi rang differ cluster techniqu consensus unsupervis classif algorithm success use categoris patient specif group often expens classifi whole set known fuzzi methodolog can provid linguist base classif rule object studi investig use fuzzi methodolog creat easi interpret set classif rule capabl place larg major patient one specifi groupsmateri method paper extend datadriven fuzzi rulebas system classif purpos call fuzzi quantif subsethoodbas algorithm combin novel class assign procedur whole approach appli well characteris breast cancer dataset consist ten protein marker patient refin previous identifi group present clinician linguist ruleset rang statist approach use compar obtain class previous obtain group assess proport unclassifi patientsresult rule set obtain algorithm featur one classif rule per class use label high low omit biomark determin appropri class patient appli whole set patient distribut obtain class agreement assess use kendal tau origin refer class distribut patient remain unclassifi repres clinic usabl class assign algorithmconclus fuzzi algorithm provid simpl interpret linguist rule set classifi breast cancer patient one seven clinic group

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