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Falling in the elderly: Do statistical models matter for performance criteria of fall prediction? Results from two large population-based studies

  • Anastasiia Kabeshova
    Affiliations
    Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France

    Computational Mathematics and Mathematical Cybernetics Department, Faculty of Applied Mathematics, OlesHonchar Dnepropetrovsk National University, Dnepropetrovsk, Ukraine
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  • Cyrille P. Launay
    Affiliations
    Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France
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  • Vasilii A. Gromov
    Affiliations
    Computational Mathematics and Mathematical Cybernetics Department, Faculty of Applied Mathematics, OlesHonchar Dnepropetrovsk National University, Dnepropetrovsk, Ukraine
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  • Bruno Fantino
    Affiliations
    Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, Angers, France
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  • Elise J. Levinoff
    Affiliations
    Department of Medicine, Division of Geriatric Medicine, Sir Mortimer B. Davis–Jewish General Hospital and Lady Davis Institute for Medical Research, McGill University, Montreal, QC, Canada
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  • Gilles Allali
    Affiliations
    Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA

    Department of Neurology, Geneva University Hospital and University of Geneva, Switzerland
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  • Olivier Beauchet
    Correspondence
    Corresponding author at: Department of Medicine, Division of Geriatric Medicine, Sir Mortimer B. Davis–Jewish General Hospital, McGill University, 3755 chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1E2, Canada. Tel.: +1 514 340 8222x4741; fax: +1 514 340 7547.
    Affiliations
    Department of Medicine, Division of Geriatric Medicine, Sir Mortimer B. Davis–Jewish General Hospital and Lady Davis Institute for Medical Research, McGill University, Montreal, QC, Canada

    Holder of Dr. Joseph Kaufmann Chair in Geriatric Medicine, Faculty of Medicine, McGill University, Montreal, QC, Canada

    Centre of Excellence on Aging and Chronic Diseases of McGill Integrated University Health Network, QC, Canada
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Published:December 10, 2015DOI:https://doi.org/10.1016/j.ejim.2015.11.019

      Highlights

      • An efficient fall prediction in older adults remains a challenge.
      • Artificial neural networks have the greatest performance criteria for fall prediction.
      • Sensitivity and specificity of artificial neural networks were unbalanced.
      • Neuroevolution of augmenting topologies should be used for the screening of fallers.
      • Adaptive neuro fuzzy interference system should be used for the diagnosis of recurrent fallers.

      Abstract

      Objective

      To compare performance criteria (i.e., sensitivity, specificity, positive predictive value, negative predictive value, area under receiver operating characteristic curve and accuracy) of linear and non-linear statistical models for fall risk in older community-dwellers.

      Methods

      Participants were recruited in two large population-based studies, “Prévention des Chutes, Réseau 4” (PCR4, n = 1760, cross-sectional design, retrospective collection of falls) and "Prévention des Chutes Personnes Agées" (PCPA, n = 1765, cohort design, prospective collection of falls). Six linear statistical models (i.e., logistic regression, discriminant analysis, Bayes network algorithm, decision tree, random forest, boosted trees), three non-linear statistical models corresponding to artificial neural networks (multilayer perceptron, genetic algorithm and neuroevolution of augmenting topologies [NEAT]) and the adaptive neuro fuzzy interference system (ANFIS) were used. Falls ≥1 characterizing fallers and falls ≥2 characterizing recurrent fallers were used as outcomes. Data of studies were analyzed separately and together.

      Results

      NEAT and ANFIS had better performance criteria compared to other models. The highest performance criteria were reported with NEAT when using PCR4 database and falls ≥1, and with both NEAT and ANFIS when pooling data together and using falls ≥2. However, sensitivity and specificity were unbalanced. Sensitivity was higher than specificity when identifying fallers, whereas the converse was found when predicting recurrent fallers.

      Conclusions

      Our results showed that NEAT and ANFIS were non-linear statistical models with the best performance criteria for the prediction of falls but their sensitivity and specificity were unbalanced, underscoring that models should be used respectively for the screening of fallers and the diagnosis of recurrent fallers.

      Abbreviations:

      ACC (Accuracy), AI (Artificial intelligence), ANFIS (Adaptive neuro fuzzy interference system), ANNS (Artificial neural networks), AUROC (Area under receiver operating characteristic curve), CHAID (Chi-squared automatic interaction detector), MLP (Multilayer perceptron), MICE (Multivariate imputation via chained equations), NPV (Negative predictive value), ANOVA (One-way analysis of variance), PCR4 (Prévention des chutes réseau 4), PCPA (Prévention des chutes personnes âgées), PPV (Positive predictive value)

      Keywords

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