The Capabilities of Artificial Neural Networks in Body Composition Research

TitelThe Capabilities of Artificial Neural Networks in Body Composition Research
Publication TypeJournal Article
Year of Publication2003
AuthorsLinder R., Mohamed E.I., De Lorenzo A., Pöppl S.J.
JournalActa diabetologica
Volume40 Suppl 1
PagesS9-14
Date Published2003 Oct
Publication Languageeng
ISSN0940-5429
SchlüsselwörterBody Composition, Diet, Humans, Models, Biological, Neural Networks (Computer), Obesity, Sports
Abstract

When estimating in vivo body composition or combining such estimates with other results, multiple variables must be taken into account (e. g. binary attributes such as gender or continuous attributes such as most biosignals). Standard statistical models, such as logistic regression and multivariate analysis, presume well-defined distributions (e. g. normal distribution); they also presume independence among all inputs and only linear relationships, yet rarely are these requirements met in real life. As an alternative to these models, artificial neural networks can be used. In the present work, we describe the pre-processing and multivariate analysis of data using neural network techniques, providing examples from the medical field and making comparisons with classic statistical approaches. We also address the criticisms raised regarding neural network techniques and discuss their potential improvement.

DOI10.1007/s00592-003-0018-x
PubMed Link

http://www.ncbi.nlm.nih.gov/pubmed/14618425?dopt=Abstract

Alternate JournalActa Diabetol
Erstellt am 9. November 2012 - 16:14 von Kulbe.

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