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Research article summary:
A new approach to training back-propagation artificial neural networks: empirical evaluation on ten data sets from clinical studies.
Abstract Extract:
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 ... (Full abstract text below) Published 2002May
in Journal: Stat Med
(Language : eng)
Full Pubmed Extract
This information was retrieved, real-time, on your behalf from the public area of the Pubmed website:
1. Stat Med.
2002 May;21(9):1309-30
A new approach to training back-propagation artificial neural networks: empirical evaluation on ten data sets from clinical studies.
Ciampi A, Zhang F
Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Avenue West, Montreal, P.Q., H3A 1A2 Canada. antonio.ciampi@mcgill.ca
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.
PMID : 12111880 [PubMed - Indexed for MEDLINE]
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Full Author Information
| First Name | LastName | Initials |
| Antonio | Ciampi | A |
| Fulin | Zhang | F |
Affiliation: Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Avenue West, Montreal, P.Q., H3A 1A2 Canada. antonio.ciampi@mcgill.ca
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MESH categories and related page links
This article was linked to the MESH categories shown on the left below. The links on the right are related Memletics pages.
Category links from this article:- Biometry - methods
- Clinical Trials as Topic - methods
- Databases as Topic
- Female
- Humans
- Logistic Models
- Male
- Neural Networks (Computer)
- Predictive Value of Tests
- Reproducibility of Results
| | Related Memletics topics: |
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