The capabilities of artificial neural networks in body composition research.
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.
Linder
R
R
Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, D-23538, Lübeck, Germany. linder@medinf.mu-luebeck.de
Mohamed
E I
EI
De Lorenzo
A
A
Pöppl
S J
SJ
D001823:Body Composition / Q000502:physiology*; D004032:Diet; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D009765:Obesity; D013177:Sports