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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Article info
Publication history
Published online: December 10, 2015
Accepted:
November 22,
2015
Received in revised form:
November 17,
2015
Received:
July 28,
2015
Identification
Copyright
© 2015 European Federation of Internal Medicine. Published by Elsevier Inc. All rights reserved.
