A quantifier-based fuzzy classification system for breast cancer patients
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 in...
| Main Authors: | , , , , , , , |
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| Format: | Article |
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Elsevier
2013
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| Online Access: | https://eprints.nottingham.ac.uk/28152/ |
| _version_ | 1848793517328957440 |
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| author | Soria, Daniele Garibaldi, Jonathan M. Green, Andrew R. Powe, Desmond G. Nolan, Christopher C. Lemetre, Christophe Ball, Graham R. Ellis, Ian O. |
| author_facet | Soria, Daniele Garibaldi, Jonathan M. Green, Andrew R. Powe, Desmond G. Nolan, Christopher C. Lemetre, Christophe Ball, Graham R. Ellis, Ian O. |
| author_sort | Soria, Daniele |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:01:33Z |
| format | Article |
| id | nottingham-28152 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:01:33Z |
| publishDate | 2013 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-281522020-05-04T20:19:09Z https://eprints.nottingham.ac.uk/28152/ 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. 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. Elsevier 2013-07 Article PeerReviewed Soria, Daniele, Garibaldi, Jonathan M., Green, Andrew R., Powe, Desmond G., Nolan, Christopher C., Lemetre, Christophe, 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 http://www.sciencedirect.com/science/article/pii/S0933365713000699 doi:10.1016/j.artmed.2013.04.006 doi:10.1016/j.artmed.2013.04.006 |
| spellingShingle | Soria, Daniele Garibaldi, Jonathan M. Green, Andrew R. Powe, Desmond G. Nolan, Christopher C. Lemetre, Christophe Ball, Graham R. Ellis, Ian O. A quantifier-based fuzzy classification system for breast cancer patients |
| title | A quantifier-based fuzzy classification system for breast cancer patients |
| title_full | A quantifier-based fuzzy classification system for breast cancer patients |
| title_fullStr | A quantifier-based fuzzy classification system for breast cancer patients |
| title_full_unstemmed | A quantifier-based fuzzy classification system for breast cancer patients |
| title_short | A quantifier-based fuzzy classification system for breast cancer patients |
| title_sort | quantifier-based fuzzy classification system for breast cancer patients |
| url | https://eprints.nottingham.ac.uk/28152/ https://eprints.nottingham.ac.uk/28152/ https://eprints.nottingham.ac.uk/28152/ |