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Identifying relevant factors about work accidents in the road transport sector and the deaths relation in this scenario
Identificación de factores relevantes sobre los accidentes laborales en el sector del transporte de carga pesada y la relación de las muertes en este escenario
DOI:
https://doi.org/10.15446/dyna.v90n225.105688Palabras clave:
data mining; occupational safety and health; transport sector; work accidents (en)minería de datos; seguridad y salud en el trabajo; sector del transporte; accidentes laborales (es)
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Workers’ health and safety are a major concern in society, since work accidents have a major impact on productivity and economy. In Brazil, the accidents are officially reported through Work Accident Communication and they are available to the public. Thus, this study analyzed a balanced dataset containing 1,206 records of deaths caused by work accidents related to the transport sector. Its aim was analyzing how the deaths in the transport sector are related with the other work accident factors. To achieve this goal, twelve performance data mining techniques are compared, through five performance metrics, regarding the predictive capacity of the occurrence of deaths caused by work accidents. In this context, the XGBoost and Naïve Bayes algorithms showed the best predictive capacity. The explanatory analysis indicates that work accidents followed by death in road transport are predictable due to the severity of the injuries and vital parts of the body are affected.
La salud y la seguridad de los trabajadores es una de las principales preocupaciones de la sociedad, ya que los accidentes de trabajo tienen un gran impacto en la productividad y la economía. En Brasil, los accidentes se comunican oficialmente a través del Aviso de Accidente de Trabajo y están a disposición de la población. Así, este estudio analizó un conjunto de datos equilibrado que contiene 1.206 registros de muertes causadas por accidentes de trabajo relacionados con el sector del transporte. Su objetivo fue analizar cómo se relacionan las muertes en el sector del transporte con otros factores de la siniestralidad laboral. Para lograr este objetivo, se comparan doce técnicas de minería de datos, utilizando cinco métricas de desempeño, respecto a la capacidad predictiva de la ocurrencia de muertes por accidentes de trabajo. En este contexto, los algoritmos XGBoost y Naïve Bayes mostraron la mejor capacidad predictiva. El análisis explicativo indica que los accidentes de trabajo seguidos de muerte en el transporte por carretera son predecibles por la gravedad de las lesiones y por las partes vitales del cuerpo afectadas.
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