Analysis of Occupational Accidents: A Data Mining Study

Abstract

Introduction: Despite efforts exerted in various areas of the industry to reduce occupational accidents, the frequency of these accidents is reported to be catastrophically high. Therefore, this study was designed and conducted to analyze occupational accidents during a 3-year period. Methods: The current study was a retrospective and descriptive-analytical study carried out in four regions of Tehran between 2019-2020. The data collection instrument consisted of 818 reports of the occupational accident recorded in the Department of Labor in Tehran. Feature selection (IBM SPSS Modeler software) and binominal multiple logistic regression analysis (IBM SPSS software) were utilized in this study. Results: The means of age and experience of injured workers were  found to be 34.55±11.55 and 14.12±9.87, respectively. The highest rate of occupational accidents belonged to construction workshops (52.4%), production (24.4 %) and other public and social services activities (11.1 %). Data mining and modeling of factors affecting these occupational accidents showed that the consequence of the accidents was affected by seven factors (p<0.05). The results showed that the work experience, type of activity, number of workers, accident time on the day, type of incidence and the causes of the accident remained in the final model and were significant with the consequence of the occupational accidents (p<0.05). Conclusion: The results indicated that different parameters can affect the occurrence of occupational accidents. Additionally, the consequences of these occupational accidents can be influenced by different parameters and factors.