A Hybrid of Structural Equation Modeling and Artificial Neural Networks to Predict Motorcyclists' Injuries: A Conceptual Model in a Case-Control Study.
Motorcyclists
Neural networks
Structural equation modeling
Traffic injury
Journal
Iranian journal of public health
ISSN: 2251-6093
Titre abrégé: Iran J Public Health
Pays: Iran
ID NLM: 7505531
Informations de publication
Date de publication:
Nov 2020
Nov 2020
Historique:
entrez:
12
3
2021
pubmed:
13
3
2021
medline:
13
3
2021
Statut:
ppublish
Résumé
To model, the predictors of injuries caused the hospitalization of motorcyclists using a hybrid structural equation modeling-artificial neural network (SEM-ANN) considering a conceptual model. In this case-control study, 300 cases and 156 controls were enrolled using a cluster random sampling. The cases were selected among injured motorcyclists in refereed to Imam Reza Hospital and Tabriz Shohada Hospital, Tabriz, Iran since Mar 2013. The predictability of injury by motorcycle-riding behavior questionnaire (MRBQ), Attention-deficit/hyperactivity disorder (ADHD) along with its subscales and motorcycle related variables was modeled using SEM-ANN. By SEM, linear direct and indirect relationships were assessed. To improve the SEM, the ANN was utilized sequentially to account for the nonlinear and interaction effects that is not supported by SEM. The predictors of injury were: MRBQ, ADHD, and its subscales, marital status, education level, riding for fun, engine volume, hyper active child, dark hour riding, cell phone answering, driving license (All The hybrid model provided results that are more accurate; considering the results of the modeling, having intervention programs on ADHD motorcyclists, those have the hyperactive child, and those who answer their cell phones while driving, and improving the motorcyclists' goal is highly recommended.
Sections du résumé
BACKGROUND
BACKGROUND
To model, the predictors of injuries caused the hospitalization of motorcyclists using a hybrid structural equation modeling-artificial neural network (SEM-ANN) considering a conceptual model.
METHODS
METHODS
In this case-control study, 300 cases and 156 controls were enrolled using a cluster random sampling. The cases were selected among injured motorcyclists in refereed to Imam Reza Hospital and Tabriz Shohada Hospital, Tabriz, Iran since Mar 2013. The predictability of injury by motorcycle-riding behavior questionnaire (MRBQ), Attention-deficit/hyperactivity disorder (ADHD) along with its subscales and motorcycle related variables was modeled using SEM-ANN. By SEM, linear direct and indirect relationships were assessed. To improve the SEM, the ANN was utilized sequentially to account for the nonlinear and interaction effects that is not supported by SEM.
RESULTS
RESULTS
The predictors of injury were: MRBQ, ADHD, and its subscales, marital status, education level, riding for fun, engine volume, hyper active child, dark hour riding, cell phone answering, driving license (All
CONCLUSION
CONCLUSIONS
The hybrid model provided results that are more accurate; considering the results of the modeling, having intervention programs on ADHD motorcyclists, those have the hyperactive child, and those who answer their cell phones while driving, and improving the motorcyclists' goal is highly recommended.
Identifiants
pubmed: 33708741
doi: 10.18502/ijph.v49i11.4738
pii: IJPH-49-2194
pmc: PMC7917492
doi:
Types de publication
Journal Article
Langues
eng
Pagination
2194-2204Informations de copyright
Copyright © 2020 Hasanzadeh et al. Published by Tehran University of Medical Sciences.
Déclaration de conflit d'intérêts
Conflict of interest The authors declare that there is no conflict of interest.
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