alexa Artificial neural networks as statistical tools in epidemiological studies: analysis of risk factors for early infant wheeze.
General Science

General Science

Forest Research: Open Access

Author(s): Sherriff A, Ott J ALSPAC Study Tea

Abstract Share this page

Abstract Artificial neural networks (ANNs) are being used increasingly for the prediction of clinical outcomes and classification of disease phenotypes. A lack of understanding of the statistical principles underlying ANNs has led to widespread misuse of these tools in the biomedical arena. In this paper, the authors compare the performance of ANNs with that of conventional linear logistic regression models in an epidemiological study of infant wheeze. Data on the putative risk factors for infant wheeze have been obtained from a sample of 7318 infants taking part in the Avon Longitudinal Study of Parents and Children (ALSPAC). The data were analysed using logistic regression models and ANNs, and performance based on misclassification rates of a validation data set were compared. Misclassification rates in the training data set decreased as the complexity of the ANN increased: h = 0: 17.9\%; h = 2: 16.2\%; h = 5: 14.9\%, and h = 10: 9.2\%. However, the more complex models did not generalise well to new data sets drawn from the same population: validation data set misclassification rates: h = 0: 17.9\%; h = 2: 19.6\%; h = 5: 20.2\% and h = 10: 22.9\%. There is no evidence from this study that ANNs outperform conventional methods of analysing epidemiological data. Increasing the complexity of the models serves only to overfit the model to the data. It is important that a validation or test data set is used to assess the performance of highly complex ANNs to avoid overfitting. This article was published in Paediatr Perinat Epidemiol and referenced in Forest Research: Open Access

Relevant Expert PPTs

Relevant Speaker PPTs

Recommended Conferences

Relevant Topics

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

1-702-714-7001Extn: 9037

Business & Management Journals


1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

1-702-714-7001 Extn: 9042

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version