Validation of a quantifier-based fuzzy classification system for breast cancer patients on external independent cohorts

Soria, Daniele; Garibaldi, Jonathan M.

Abstract

Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categorise patients into these specific groups, but often at the expenses of not classifying all patients. It is known that fuzzy methodologies can provide linguistic based classification rules to ease those from consensus clustering. The objective of this study is to present the validation of a recently developed extension of a fuzzy quantification subsethood-based algorithm on three sets of newly available breast cancer data. Results show that our algorithm is able to reproduce the seven biological classes previously identified, preserving their characterisation in terms of marker distributions and therefore their clinical meaning. Moreover, because our algorithm constitutes the fundamental basis of the newly developed Nottingham Prognostic Index Plus (NPI+), our findings demonstrate that this new medical decision making tool can help moving towards a more tailored care in breast cancer.

Citation

Soria, D., & Garibaldi, J. M. (2016). Validation of a quantifier-based fuzzy classification system for breast cancer patients on external independent cohorts.

• Conference Name: International Conference on Machine Learning and Applications
• End Date: Dec 20, 2016
• Acceptance Date: Sep 30, 2016
• Online Publication Date: Feb 2, 2017
• Publication Date: Dec 20, 2016
• Deposit Date: Feb 13, 2017
• Publicly Available Date: Feb 13, 2017
• Peer Reviewed: Peer Reviewed
• Keywords: Rule-based classification, Fuzzy rules, Validation, Breast cancer
• Public URL: https://nottingham-repository.worktribe.com/output/832845
• Publisher URL: http://ieeexplore.ieee.org/document/7838205/
• Related Public URLs: http://www.icmla-conference.org/icmla16/
• Additional Information: Published in: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). Piscataway, N.J. : IEEE, 2016. ISBN: 978-1-5090-6167-9. pp. 576-581, doi:10.1109/ICMLA.2016.0101 ©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.