An ensemble machine learning approach to predict survival in breast cancer.
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.
Djebbari
Amira
A
Knowledge Discovery, Institute for Information Technology, National Research Council Canada, 46 Dineen Drive, Fredericton, NB E3B 9W4, Canada. amira.djebbari@nrc-cnrc.gc.ca
Liu
Ziying
Z
Phan
Sieu
S
Famili
Fazel
F
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics* / Q000401:mortality*; D019295:Computational Biology; D003198:Computer Simulation; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D011379:Prognosis; D016019:Survival Analysis