An ensemble machine learning approach to predict survival in breast cancer
by Amira Djebbari, Ziying Liu, Sieu Phan, Fazel Famili
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 1, No. 3, 2008

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.

Online publication date: Wed, 26-Nov-2008

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