title: Falling in the elderly: Do statistical models matter for performance criteria of fall prediction? Results from two large population-based studies.
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.
authors.affiliation: 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.
authors.forename: Anastasiia
authors.initials: A
authors.lastname: Kabeshova
authors.affiliation: Department of Neuroscience, Division of Geriatric Medicine, 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, OlesHonchar 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.
authors.forename: Bruno
authors.initials: B
authors.lastname: Fantino
authors.affiliation: 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.
authors.forename: Elise J
authors.initials: EJ
authors.lastname: Levinoff
authors.affiliation: 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.
authors.forename: Gilles
authors.initials: G
authors.lastname: Allali
authors.affiliation: 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. Electronic address: olivier.beauchet@mcgill.ca.
authors.forename: Olivier
authors.initials: O
authors.lastname: Beauchet
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data*; D000368:Aged; D000369:Aged, 80 and over; D003430:Cross-Sectional Studies; D005260:Female; D005602:France; D015577:Geriatric Assessment / Q000379:methods*; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011379:Prognosis; D011446:Prospective Studies; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity
keywords: Accidental fall; Aged 80 and over; Artificial neural network; Prediction