Abstract:
: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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Ciampi A,Zhang Fdoi
10.1002/sim.1107subject
Has Abstractpub_date
2002-05-15 00:00:00pages
1309-30issue
9eissn
0277-6715issn
1097-0258journal_volume
21pub_type
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