Advanced

Ensembles of genetically trained artificial neural networks for survival analysis

Kalderstam, Jonas LU ; Edén, Patrik LU and Ohlsson, Mattias LU (2013) 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning p.333-338
Abstract

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.

Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
pages
6 pages
conference name
21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013
external identifiers
  • scopus:84887057609
ISBN
9782874190810
language
English
LU publication?
yes
id
34253bab-8f63-432a-9ed5-f9f254e46752
date added to LUP
2017-05-23 09:44:08
date last changed
2018-05-29 10:43:37
@inproceedings{34253bab-8f63-432a-9ed5-f9f254e46752,
  abstract     = {<p>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.</p>},
  author       = {Kalderstam, Jonas and Edén, Patrik and Ohlsson, Mattias},
  booktitle    = {ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
  isbn         = {9782874190810},
  language     = {eng},
  pages        = {333--338},
  title        = {Ensembles of genetically trained artificial neural networks for survival analysis},
  year         = {2013},
}