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Investigation of Predicting Functional Capacity Level for Huntington Disease Patients

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Information and Software Technologies (ICIST 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 756))

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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.

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Author information

Authors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Andrius Lauraitis & Rytis Maskeliūnas

Authors
  1. Andrius Lauraitis
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  2. Rytis Maskeliūnas
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Corresponding author

Correspondence to Rytis Maskeliūnas .

Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damaševičius

  2. Kaunas University of Technology, Kaunas, Lithuania

    Vilma Mikašytė

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Lauraitis, A., Maskeliūnas, R. (2017). Investigation of Predicting Functional Capacity Level for Huntington Disease Patients. In: Damaševičius, R., Mikašytė, V. (eds) Information and Software Technologies. ICIST 2017. Communications in Computer and Information Science, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-67642-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-67642-5_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67641-8

  • Online ISBN: 978-3-319-67642-5

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