An ensemble machine learning approach to predict survival in breast cancer. (1st December 2008)

• Record Type: Journal Article
• Authors: Djebbari, Amira; Liu, Ziying; Phan, Sieu; Famili, Fazel
• Is Part Of: International journal of computational biology and drug design. Volume 1:Number 3(2008)
• Page Start: 275
• Page End: 294
• Publication Date: 2008-12-01
• Subjects: computational biology -- machine learning -- data mining -- knowledge discovery -- bioinformatics -- breast cancer prognosis -- survival prediction -- classification performance -- sensitivity; Computational biology -- Periodicals; Drugs -- Design -- Periodicals; 570.285
• Languages: English
• ISSNs: 1756-0756

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.