An ensemble machine learning approach to predict survival in breast cancer

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DOIResolve DOI: http://doi.org/10.1504/IJCBDD.2008.021422
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TypeArticle
Journal titleInternational Journal of Computational Biology and Drug Design
ISSN1756-0756
1756-0764
Volume1
Issue3
Pages275294
Subjectcomputational biology; machine learning; data mining; knowledge discovery; bioinformatics; breast cancer prognosis; survival prediction; classification performance; sensitivity
AbstractCurrent breast cancer predictive signatures are not unique. Can we use this fact to our advantage to improve prediction? From the machine learning perspective, it is well known that combining multiple classifiers can improve classification performance. We propose an ensemble machine learning approach which consists of choosing feature subsets and learning predictive models from them. We then combine models based on certain model fusion criteria and we also introduce a tuning parameter to control sensitivity. Our method significantly improves classification performance with a particular emphasis on sensitivity which is critical to avoid misclassifying poor prognosis patients as good prognosis.
Publication date
PublisherInderscience
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedYes
NPARC number23000650
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Record identifier8144431a-396f-4818-b3f7-9eaf614d1011
Record created2016-08-17
Record modified2016-08-17
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