A new approach to training back-propagation artificial neural networks: empirical evaluation on ten data sets from clinical studies.
We present a new approach to training back-propagation artificial neural nets (BP-ANN) based on regularization and cross-validation and on initialization by a logistic regression (LR) model. The new approach is expected to produce a BP-ANN predictor at least as good as the LR-based one. We have applied the approach to ten data sets of biomedical interest and systematically compared BP-ANN and LR. In all data sets, taking deviance as criterion, the BP-ANN predictor outperforms the LR predictor used in the initialization, and in six cases the improvement is statistically significant. The other evaluation criteria used (C-index, MSE and error rate) yield variable results, but, on the whole, confirm that, in practical situations of clinical interest, proper training may significantly improve the predictive performance of a BP-ANN.
Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Avenue West, Montreal, P.Q., H3A 1A2 Canada. antonio.ciampi@mcgill.ca
Antonio
A
Ciampi
Fulin
F
Zhang
D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic / Q000379:methods; D019992:Databases as Topic; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015203:Reproducibility of Results