Investigation of Predicting Functional Capacity Level for Huntington Disease Patients View Full Text


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Chapter Info

DATE

2017-09-23

AUTHORS

Andrius Lauraitis , Rytis Maskeliūnas

ABSTRACT

This paper introduces a model to forecast functional capacity level for people having disorders such as hand tremors, disturbed balance, involuntary movements, chorea etc. These motor features are very closely related the symptoms occurring for Huntington or Parkinson patients in various stages of the disease. Proposed model is designed by applying one of supervised learning artificial neural network models for data collected with smart phones or tablets. Feed-forward backpropagation (FFBP), feed-forward time delay neural network (FFTDNN), cascade forward backpropagation (CFBP), nonlinear autoregressive exogenous model (NARX), Elman, layer recurrent neural network (RNN) and generalized regression neural network (GRNN) were used in investigation. Moreover, the processes of preparing and labeling data, choosing a learning algorithm, training particular neural network, evaluating and comparing each model performance, making predictions on new data, are described in the paper. More... »

PAGES

142-149

Book

TITLE

Information and Software Technologies

ISBN

978-3-319-67641-8
978-3-319-67642-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-67642-5_12

DOI

http://dx.doi.org/10.1007/978-3-319-67642-5_12

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100749287


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