A new approach to training back-propagation artificial neural networks: empirical evaluation on ten data sets from clinical studies.

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 Med

journal_title

Statistics in medicine

authors

Ciampi A,Zhang F

doi

10.1002/sim.1107

subject

Has Abstract

pub_date

2002-05-15 00:00:00

pages

1309-30

issue

9

eissn

0277-6715

issn

1097-0258

journal_volume

21

pub_type

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