Title: An ensemble machine learning approach to predict survival in breast cancer

Authors: Amira Djebbari, Ziying Liu, Sieu Phan, Fazel Famili

Addresses: Knowledge Discovery, Institute for Information Technology, National Research Council Canada, 46 Dineen Drive, Fredericton, NB E3B 9W4, Canada. ' Knowledge Discovery, Institute for Information Technology, National Research Council Canada, 1200 Montreal Road, Building M-50, Room 367, Ottawa, ON K1A 0R6, Canada. ' Knowledge Discovery, Institute for Information Technology, National Research Council Canada, 1200 Montreal Road, Building M-50, Room 372A, Ottawa, ON K1A 0R6, Canada. ' Knowledge Discovery, Institute for Information Technology, National Research Council Canada, 1200 Montreal Road, Building M-50, Room 366, Ottawa, ON K1A 0R6, Canada

Abstract: Current 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.

Keywords: computational biology; machine learning; data mining; knowledge discovery; bioinformatics; breast cancer prognosis; survival prediction; classification performance; sensitivity.

DOI: 10.1504/IJCBDD.2008.021422

International Journal of Computational Biology and Drug Design, 2008 Vol.1 No.3, pp.275 - 294

Available online: 26 Nov 2008 *

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