Identifying the Factors Affecting Occupational Accidents: An Artificial Neural Network Model

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.