title: Use of Artificial Neural Networks (ANNs) for the Analysis and Modeling of Factors That Affect Occupational Injuries in Large Construction Industries.
abstract: BACKGROUND
  Occupational injuries as a workforce's health problem are very important in large-scale workplaces. Analysis and modeling the health-threatening factors are good ways to promote the workforce's health and a fundamental step in developing health programs. The purpose of this study was ANN modeling of the severity of occupational injuries to determine the health-threatening factors and to introduce a model to predict the severity of occupational injuries.
  
  
  METHODS
  This analytical chain study was conducted in 10 large construction industries during a 10-year period (2005-2014). Nine hundred sixty occupational injuries were analyzed and modeled based on feature weighting by the rough set theory and artificial neural networks (ANNs). Two analytical software programs, i.e., RSES and MATLAB 2014 were used in the study.
  
  
  RESULTS
  The severity of occupational injuries was calculated as 557.47 ± 397.87 days. The findings of both models showed that the injuries' severity as a health problem resulted in various factors, including individual, organizational, health and safety (H&S) training, and risk management factors, which could be considered as causal and predictive factors of accident severity rate (ASR).
  
  
  CONCLUSIONS
  The results indicated that ANNs were a reliable tool that can be used to analyze and model the severity of occupational injuries as one of the important health problems in large-scale workplaces. Additionally, the combination of rough set and ANNs is a good and proper chain approach to modeling the factors that threaten the health of workforces and other H&S problems.
authors.affiliation: Ph.D. of Occupational Hygiene Engineering, Associate Professor, Department of Occupational Hygiene Engineering, School of Public Health and Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran.
authors.forename: Iraj
authors.initials: I
authors.lastname: Mohammadfam
authors.affiliation: Ph.D. Candidate of Occupational Hygiene Engineering, Department of Occupational Hygiene Engineering, School of Public Health and Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran.
authors.forename: Ahmad
authors.initials: A
authors.lastname: Soltanzadeh
authors.affiliation: Ph.D. of Biostatistics, Associate Professor, Department of Biostatistics and Epidemiology and Modeling of Non-communicable Disease Research Canter, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
authors.forename: Abbas
authors.initials: A
authors.lastname: Moghimbeigi
authors.affiliation: Ph.D. Candidate of Medical Informatics, Department of Medical Informatics, Allied Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Behrouz Alizadeh
authors.initials: BA
authors.lastname: Savareh
keywords: accident severity rate (ASR); artificial neural networks (ANN); occupational injury; rough set theory; workforce’s health