Identifying the factors affecting occupational accidents: An artificial neural network mode


1 Faculty of Health, Baqiyatallah University of Medical Sciences, Tehran, Iran

2 Exercise Physiology Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran

3 Department of Occupational Health Engineering, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran

4 Department of Occupational Health, School of Health, Kashan University of Medical Sciences, Kashan, Iran

5 Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran


Background and Objectives: Occupational accidents impose high costs on organizations annually. This study aimed at investigating the factors affecting military work-related accidents using artificial neural network (ANN) and Bayesian models. Materials and Methods: This study was a cross-sectional survey in a military unit that examined all occupational accidents recorded during 2011–2018. First, we collected the data of the accidents using the accident database in the inspection sector of the Department of Health and the Medical Commission of the Armed Forces. ANN, Bayesian, and logistic regression models were used to analyze the data. Results: The results of the type of accidents showed that 219 cases of sport accidents (32.8%), 125 cases fall from height (18.7%), and 104 cases of driving accidents (15.6%) were the most common accidents. Based on the results of multivariate regression, accident variables due to fighting (odds ratio [OR] =17.21), injury to the body or back (OR = 122.55), and multiple injuries (OR = 25.72) were considered as influential and significant factors. The ANNs results showed that the highest importance factor was the injury to the body or back, multiple injuries, age, fighting, and finally, driving accident. Furthermore, the Bayesian model showed that the most important factors affecting the death consequence due to accidents were related to injuries to the body or back (OR = 276.23), multiple injuries (OR = 54.98), and accidents due to conflict (OR = 33.69). Conclusion: The findings show that the most important factors affecting the death consequence due to accidents in the military are the injury to the whole body, multiple injuries, age, fighting accident, and driving accident. The ANN and Bayesian models have provided more accurate information than logistic regression based on the obtained results.


  1. Rushton L. The global burden of occupational disease. Curr Environ Health Rep 2017;4:340‑8.
    2. Yadollahi M, Gholamzadeh S. Five‑year forecasting deaths caused by traffic accidents in fars province of Iran. Bull Emerg Trauma 2019;7:373‑80.
    3. Asady H, Yaseri M, Hosseini M, Zarif‑Yeganeh M, Yousefifard M, Haghshenas M, et al. Risk factors of fatal occupational accidents in Iran. Ann Occup Environ Med 2018;30:29.
    4. Ghamari F, Mohammadfam I, Mohammadbeigi A, Ebrahimi H, Khodayari M. Determination of effective risk factors in incidence of occupational accidents in one of the large metal industries, Arak (2005‑2007). Iran Occup Health 2012;9:89‑96.
    5. Driscoll T, Takala J, Steenland K, Corvalan C, Fingerhut M. Review of estimates of the global burden of injury and illness due to occupational exposures. Am J Ind Med 2005;48:491‑502.
    6. Zarocostas J. International Labour Organisation tackles work related injuries. BMJ 2005;331:656.
    7. Kivimäki M, Virtanen M, Nyberg ST, Batty GD. The WHO/ILO report on long working hours and ischaemic heart disease – Conclusions are not supported by the evidence. Environ Int 2020;144:106048.
    8. Izadi N, Aminian O, Esmaeili B. Occupational accidents in Iran: Risk factors and long term trend (2007‑2016). J Res Health Sci 2019;19:e00448.
    9. Stricklin DL. Risk assessment in international operations. Toxicol Appl Figure 3: The importance of each variable in the artificial neural network model[Downloaded free from on Wednesday, September 7, 2022, IP:]Hassanipour, et al.: Identifying the factors affecting occupational accidents202 Archives of Trauma Research ¦ Volume 10 ¦ Issue 4 ¦ October‑December 2021Pharmacol 2008;233:107‑9.
  2. 10. Malliarou M, Sourtzi P, Galanis P, Constantinidis T, Velonakis E. Occupational accidents in Greek armed forces in Evros County. BMJ Mil Health 2012;158:313‑7.
    11. Lazarus RS, Folkman S. Transactional theory and research on emotions and coping. Eur J Pers 1987;1:141‑69.
    12. Khosravi S, Ghafari M. Epidemiological study of domestic accidents in urban and rural area of Shahrekord in 1999. J Shahrekord Univ Med Sci 2003;5:53-64.
    13. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today 2021;26:80‑93.
    14. Ahmed FE. Artificial neural networks for diagnosis and survival prediction in colon cancer. Mol Cancer 2005;4:29.
    15. Mathur P, Srivastava S, Xu X, Mehta JL. Artificial intelligence, machine learning, and cardiovascular disease. Clin Med Insights Cardiol 2020;14:1-9.
    16. Carriger JF, Yee SH, Fisher WS. An introduction to Bayesian networks as assessment and decision support tools for managing coral reef ecosystem services. Ocean Coast Manag 2019;177:188‑99.
    17. Gelman A, Shalizi CR. Philosophy and the practice of Bayesian statistics. Br J Math Stat Psychol 2013;66:8‑38.
    18. Sperandei S. Understanding logistic regression analysis. Biochem Med (Zagreb) 2014;24:12‑8.
    19. Anderson RP, Jin R, Grunkemeier GL. Understanding logistic regression analysis in clinical reports: An introduction. Ann Thorac Surg 2003;75:753‑7.
    20. Hassanipour S, Ghaem H, Arab‑Zozani M, Seif M, Fararouei M, Abdzadeh E, et al. Comparison of artificial neural network and logistic regression models for prediction of outcomes in trauma patients: A systematic review and meta‑analysis. Injury 2019;50:244‑50.
    21. Jones BH, Perrotta DM, Canham‑Chervak ML, Nee MA, Brundage JF. Injuries in the military: Areview and commentary focused on prevention. Am J Prev Med 2000;18:71‑84.
    22. Hua W, Chen Q, Wan M, Lu J, Xiong L. The incidence of military training‑related injuries in Chinese new recruits: A systematic review and meta‑analysis. J R Army Med Corps 2018;164:309‑13.
    23. Patel JL, Goyal RK. Applications of artificial neural networks in medical science. Curr Clin Pharmacol 2007;2:217‑26.
    24. Jiang J, Trundle P, Ren J. Medical image analysis with artificial neural networks. Comput Med Imaging Graph 2010;34:617‑31.
    25. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw 1989;2:359‑66.
    26. Bakhtiyari M, Delpisheh A, Riahi SM, Latifi A, Zayeri F, Salehi M,et al. Epidemiology of occupational accidents among Iranian insured workers. Saf Sci 2012;50:1480‑4.
    27. Reger MA, Smolenski DJ, Skopp NA, Metzger‑Abamukang MJ, Kang HK, Bullman TA, et al. Suicides, homicides, accidents, and undetermined deaths in the U.S. military: Comparisons to the U.S. population and by military separation status. Ann Epidemiol 2018;28:139‑46.e1.
    28. Bakhtiyari M, Aghaie A, Delpisheh A, Akbarpour S, Zayeri F, Soori H,et al. An epidemiologic survey of recorded job‑related accidents by Iranian social security organization (2001‑2005). J Rafsanjan Univ Med 
    Sci 2012;11:231‑46.
    29. Bergman BP, Mackay DF, Pell JP. Road traffic accidents in Scottish military veterans. Accid Anal Prev 2018;113:287‑91.
    30. Saadat S, Soori H. Epidemiology of traffic injuries and motor vehicles utilization in the capital of Iran: A population based study. BMC Public Health 2011;11:488.
    31. Hatamabadi H, Vafaee R, Hadadi M, Abdalvand A, Esnaashari H, Soori H. Epidemiologic study of road traffic injuries by road user type characteristics and road environment in Iran: A community‑based 
    approach. Traffic Inj Prev 2012;13:61‑4.
    32. Mokhtari AM, Samadi S, Hatami SE, Jalilian H, Khanjani N. Investigating the rate of helmet use and the related factors among motorcyclists in Kerman between 1391‑92 (2012). Saf Promot Inj 
    Prev (Tehran) 2014;2:209‑14.
    33. Pfeifer R, Teuben M, Andruszkow H, Barkatali BM, Pape HC. Mortality patterns in patients with multiple trauma: Asystematic review of autopsy studies. PLoS One 2016;11:e0148844.
    34. Bone LB, McNamara K, Shine B, Border J. Mortality in multiple trauma patients with fractures. J Trauma 1994;37:262‑4.
    35. Rughani AI, Dumont TM, Lu Z, Bongard J, Horgan MA, Penar PL,et al. Use of an artificial neural network to predict head injury outcome. J Neurosurg 2010;113:585‑90.
    36. Shi HY, Hwang SL, Lee KT, Lin CL. In‑hospital mortality after traumatic brain injury surgery: A nationwide population‑based comparison of mortality predictors used in artificial neural network and 
    logistic regression models. J Neurosurg 2013;118:746‑52.
    37. Lang EW, Pitts LH, Damron SL, Rutledge R. Outcome after severe head injury: An analysis of prediction based upon comparison of neural network versus logistic regression analysis. Neurol Res 1997;19:274‑80.
    38. Sargent DJ. Comparison of artificial neural networks with other statistical approaches: Results from medical data sets. Cancer 2001;91:1636‑42.