@article{ref1,
title="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",
year="2014",
author="Kabeshova, Anastasiia and Launay, Cyrille P. and Gromov, Vasilii A. and Annweiler, Cédric and Fantino, Bruno and Beauchet, Olivier",
volume="16",
number="4",
pages="277-281",
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. <br><br>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. <br><br>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. <br><br>CONCLUSIONS: NEAT, using a set of 15 clinical characteristics, was an efficient ANN for the identification of recurrent fallers in older community-dwellers.<p /> <p>Language: en</p>",
language="en",
issn="1525-8610",
doi="10.1016/j.jamda.2014.09.013",
url="http://dx.doi.org/10.1016/j.jamda.2014.09.013"
}