title: Artificial neural network and falls in community-dwellers: a new approach to identify the risk of recurrent falling?
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.
  
  
  METHODS
  Based on a cross-sectional design, 3289 community-dwelling volunteers aged 65 and older were recruited. Age, gender, body mass index (BMI), number of drugs daily taken, use of psychoactive drugs, diphosphonate, calcium, vitamin D supplements and walking aid, fear of falling, distance vision score, Timed Up and Go (TUG) score, lower-limb proprioception, handgrip strength, depressive symptoms, cognitive disorders, and history of falls were recorded. Participants were separated into 2 groups based on the number of falls that occurred over the past year: 0 or 1 fall and 2 or more falls. In addition, total population was separated into training and testing subgroups for ANN analysis.
  
  
  RESULTS
  Among 3289 participants, 18.9% (n = 622) were recurrent fallers. NEAT, using 15 clinical characteristics (ie, use of walking aid, fear of falling, use of calcium, depression, use of vitamin D supplements, female, cognitive disorders, BMI <21 kg/m(2), number of drugs daily taken >4, vision score <8, use of psychoactive drugs, lower-limb proprioception score ≤5, TUG score >9 seconds, handgrip strength score ≤29 (N), and age ≥75 years), showed the best efficiency for identification of recurrent fallers, sensitivity (80.42%), specificity (92.54%), positive predictive value (84.38), negative predictive value (90.34), accuracy (88.39), and Cohen κ (0.74), compared with MLP and modified MLP.
  
  
  CONCLUSIONS
  NEAT, using a set of 15 clinical characteristics, was an efficient ANN for the identification of recurrent fallers in older community-dwellers.
authors.affiliation: 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.
authors.forename: Anastasiia
authors.initials: A
authors.lastname: Kabeshova
authors.affiliation: Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France; UPRES EA 4336, UNAM, Angers University Hospital, Angers, France.
authors.forename: Cyrille P
authors.initials: CP
authors.lastname: Launay
authors.affiliation: Computational Mathematics and Mathematical Cybernetics Department, Faculty of Applied Mathematics, Oles Honchar Dnepropetrovsk National University Dnepropetrovsk, Ukraine.
authors.forename: Vasilii A
authors.initials: VA
authors.lastname: Gromov
authors.affiliation: 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.
authors.forename: Cédric
authors.initials: C
authors.lastname: Annweiler
authors.affiliation: Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France.
authors.forename: Bruno
authors.initials: B
authors.lastname: Fantino
authors.affiliation: Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France. Electronic address: olbeauchet@chu-angers.fr.
authors.forename: Olivier
authors.initials: O
authors.lastname: Beauchet
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control* / Q000706:statistics & numerical data*; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D003430:Cross-Sectional Studies; D005260:Female; D015577:Geriatric Assessment / Q000379:methods*; D006801:Humans; D015994:Incidence; D057187:Independent Living; D008297:Male; D016571:Neural Networks, Computer; D012008:Recurrence; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D012737:Sex Factors
keywords: Recurrent fall; artificial neural network; elderly