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Photo of Mattias Ohlsson

Mattias Ohlsson

Professor

Photo of Mattias Ohlsson

Ensembles of genetically trained artificial neural networks for survival analysis

Author

  • Jonas Kalderstam
  • Patrik Edén
  • Mattias Ohlsson

Summary, in English

We have developed a prognostic index model for survival data based on an ensemble of artificial neural networks that optimizes directly on the concordance index. Approximations of the c-index are avoided with the use of a genetic algorithm, which does not require gradient information. The model is compared with Cox proportional hazards (COX) and three support vector machine (SVM) models by Van Belle et al. [10] on two clinical data sets, and only with COX on one artificial data set. Results indicate comparable performance to COX and SVM models on clinical data and superior performance compared to COX on non-linear data.

Department/s

  • Computational Biology and Biological Physics - Has been reorganised

Publishing year

2013

Language

English

Pages

333-338

Publication/Series

ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Document type

Conference paper

Topic

  • Other Computer and Information Science

Conference name

21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013

Conference date

2013-04-24 - 2013-04-26

Conference place

Bruges, Belgium

Status

Published

ISBN/ISSN/Other

  • ISBN: 9782874190810