A Hybrid of Structural Equation Modeling and Artificial Neural Networks to Predict Motorcyclists’ Injuries: A Conceptual Model in a Case-Control Study

Abstract

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: In this case-control study, 300 cases and 156 controls were enrolled using a cluster random sam- pling. 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 motor- cycle 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: The predictors of injury were: MRBQ, ADHD, and its subscales, marital status, education level, rid- ing for fun, engine volume, hyper active child, dark hour riding, cell phone answering, driving license (All P less than 0.05). In addition, the findings reveal the Mediating role of MRBQ for the relationship between underly- ing predictors and injury. Furthermore, ANN showed higher specificity (95.45 vs.77.88) and accuracy (90.76 vs.79.94) than usual SEM which lead us to introduce the second and third order effect of MRBQ into the modified SEM.
Conclusion: The hybrid model provided results that are more accurate; considering the results of the model- ing, 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.