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 language | English |
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Title of host publication | ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
Pages | 333-338 |
Number of pages | 6 |
Publication status | Published - 2013 |
Event | 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 - Bruges, Belgium Duration: 2013 Apr 24 → 2013 Apr 26 |
Conference
Conference | 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 |
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Country/Territory | Belgium |
City | Bruges |
Period | 2013/04/24 → 2013/04/26 |
Subject classification (UKÄ)
- Other Computer and Information Science