Ensembles of genetically trained artificial neural networks for survival analysis

Jonas Kalderstam, Patrik Edén, Mattias Ohlsson

    Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

    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.

    Original languageEnglish
    Title of host publicationESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
    Pages333-338
    Number of pages6
    Publication statusPublished - 2013
    Event21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 - Bruges, Belgium
    Duration: 2013 Apr 242013 Apr 26

    Conference

    Conference21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013
    Country/TerritoryBelgium
    CityBruges
    Period2013/04/242013/04/26

    Subject classification (UKÄ)

    • Other Computer and Information Science

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