title: Training artificial neural networks directly on the concordance index for censored data using genetic algorithms.
abstract: OBJECTIVE
  The concordance index (c-index) is the standard way of evaluating the performance of prognostic models in the presence of censored data. Constructing prognostic models using artificial neural networks (ANNs) is commonly done by training on error functions which are modified versions of the c-index. Our objective was to demonstrate the capability of training directly on the c-index and to evaluate our approach compared to the Cox proportional hazards model.
  
  
  METHODS
  We constructed a prognostic model using an ensemble of ANNs which were trained using a genetic algorithm. The individual networks were trained on a non-linear artificial data set divided into a training and test set both of size 2000, where 50% of the data was censored. The ANNs were also trained on a data set consisting of 4042 patients treated for breast cancer spread over five different medical studies, 2/3 used for training and 1/3 used as a test set. A Cox model was also constructed on the same data in both cases. The two models' c-indices on the test sets were then compared. The ranking performance of the models is additionally presented visually using modified scatter plots.
  
  
  RESULTS
  Cross validation on the cancer training set did not indicate any non-linear effects between the covariates. An ensemble of 30 ANNs with one hidden neuron was therefore used. The ANN model had almost the same c-index score as the Cox model (c-index=0.70 and 0.71, respectively) on the cancer test set. Both models identified similarly sized low risk groups with at most 10% false positives, 49 for the ANN model and 60 for the Cox model, but repeated bootstrap runs indicate that the difference was not significant. A significant difference could however be seen when applied on the non-linear synthetic data set. In that case the ANN ensemble managed to achieve a c-index score of 0.90 whereas the Cox model failed to distinguish itself from the random case (c-index=0.49).
  
  
  CONCLUSIONS
  We have found empirical evidence that ensembles of ANN models can be optimized directly on the c-index. Comparison with a Cox model indicates that near identical performance is achieved on a real cancer data set while on a non-linear data set the ANN model is clearly superior.
authors.affiliation: Computational Biology and Biological Physics Group, Department of Astronomy and Theoretical Physics, Lund University, Sölvegatan 14A, SE-22362 Lund, Sweden. jonask@thep.lu.se
authors.forename: Jonas
authors.initials: J
authors.lastname: Kalderstam
authors.forename: Patrik
authors.initials: P
authors.lastname: Edén
authors.forename: Pär-Ola
authors.initials: PO
authors.lastname: Bendahl
authors.forename: Carina
authors.initials: C
authors.lastname: Strand
authors.forename: Mårten
authors.initials: M
authors.lastname: Fernö
authors.forename: Mattias
authors.initials: M
authors.lastname: Ohlsson
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000401:mortality / Q000473:pathology; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D009364:Neoplasm Recurrence, Local; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D016016:Proportional Hazards Models; D013997:Time Factors