Identifying relevant factors about work accidents in the road transport sector and the deaths relation in this scenario

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 con...

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Published inDyna (Medellín, Colombia) Vol. 90; no. 225; pp. 105 - 110
Main Authors Ferreira da Silva, Lucimara, Lavezo dos Reis, Beatriz, Dos Santos Jesus, Liandra, Leal, Gislaine Camila Lapasini, Cardoza Galdamez, Edwin Vladimir
Format Journal Article
LanguageEnglish
Published Bogota Universidad Nacional de Colombia 01.01.2023
Subjects
Accident prediction
Accident prevention
accidentes laborales
Accidents
Algorithms
Costs
Data mining
data mining; occupational safety and health; transport sector; work accidents
Fatalities
Illnesses
Injuries
Injury analysis
minería de datos
Occupational accidents
Occupational diseases
Occupational safety
occupational safety and health
Performance measurement
Performance prediction
Road transportation
Roads & highways
sector del transporte
seguridad y salud en el trabajo
transport sector
Transportation industry
work accidents
Work capacity
Work environment
Workers
Working conditions
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Abstract 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.
AbstractList 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.
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.
Author Cardoza Galdamez, Edwin Vladimir
Leal, Gislaine Camila Lapasini
Dos Santos Jesus, Liandra
Ferreira da Silva, Lucimara
Lavezo dos Reis, Beatriz
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– notice: LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. Más información: https://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by its authors or editors. Unless expressly stated otherwise in the licensing conditions, you are free to linking, browsing, printing and making a copy for your own personal purposes. All other acts of reproduction and communication to the public are subject to the licensing conditions expressed by editors and authors and require consent from them. Any link to this document should be made using its official URL in Dialnet. More info: https://dialnet.unirioja.es/info/derechosOAI
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StartPage 105
SubjectTerms Accident prediction
Accident prevention
accidentes laborales
Accidents
Algorithms
Costs
Data mining
data mining; occupational safety and health; transport sector; work accidents
Fatalities
Illnesses
Injuries
Injury analysis
minería de datos
Occupational accidents
Occupational diseases
Occupational safety
occupational safety and health
Performance measurement
Performance prediction
Road transportation
Roads & highways
sector del transporte
seguridad y salud en el trabajo
transport sector
Transportation industry
work accidents
Work capacity
Work environment
Workers
Working conditions
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