Artificial neural network and falls in community-dwellers: a new approach to identify the risk of recurrent falling?

Journal: Journal of the American Medical Directors Association
Published Date: Oct 29, 2014

Abstract

BACKGROUND: Identification of the risk of recurrent falls is complex in older adults. The aim of this study was to examine the efficiency of 3 artificial neural networks (ANNs: multilayer perceptron [MLP], modified MLP, and neuroevolution of augmenting topologies [NEAT]) for the classification of recurrent fallers and nonrecurrent fallers using a set of clinical characteristics corresponding to risk factors of falls measured among community-dwelling older adults.

Authors

  • Anastasiia Kabeshova
    Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France; UPRES EA 4336, UNAM, Angers University Hospital, Angers, France; Computational Mathematics and Mathematical Cybernetics Department, Faculty of Applied Mathematics, Oles Honchar Dnepropetrovsk National University Dnepropetrovsk, Ukraine.
  • Cyrille P Launay
    Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France; UPRES EA 4336, UNAM, Angers University Hospital, Angers, France.
  • Vasilii A Gromov
    Computational Mathematics and Mathematical Cybernetics Department, Faculty of Applied Mathematics, Oles Honchar Dnepropetrovsk National University Dnepropetrovsk, Ukraine.
  • Cédric Annweiler
    Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France; Robarts Research Institute, Department of Medical Biophysics, Schulich School of Medicine and Dentistry, the University of Western Ontario, London, Ontario, Canada.
  • Bruno Fantino
    Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France.
  • Olivier Beauchet
    Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France. Electronic address: olbeauchet@chu-angers.fr.

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