Combining Clustering and Classification Ensembles: A Novel Pipeline to Identify Breast Cancer Profiles
- Submitting institution
-
The University of Westminster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- qv3y1
- Type
- D - Journal article
- DOI
-
10.1016/j.artmed.2019.05.002
- Title of journal
- Artificial Intelligence in Medicine
- Article number
- -
- First page
- 27
- Volume
- 97
- Issue
- -
- ISSN
- 0933-3657
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
8
- Research group(s)
-
-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Previous classification studies on breast cancer had identified several groups, but still left a small proportion of patients unclassified. This work is significant, because it shows that by using an ensemble of classifiers after the clustering process, more patients can be categorised in one of the biological breast cancer groups, thus allowing them to receive the most suitable treatment in the shortest possible time. This approach could have significant impact to the NHS, after extensive validation is performed.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
