Pbkp_rediction of psychosocial occupational risk in urban transport applying machine learning techniques

Abstract:

The job of urban bus driver is among the most risky and stressful modern occupations. Although the use of information and communication technologies provides greater autonomy and flexibility at work, it also causes exposure to risks of a psychosocial nature. Machine learning techniques contribute to the early pbkp_rediction of these types of risks and contribute efficiently to decision-making. Classification is performed with the closest K-neighboring algorithms, Decision Tree and Vector Support Machine to determine the precision in the pbkp_rediction of each one. The psychosocial risk dataset of the urban passenger transport drivers of the city of Ambato is used, obtained with the Fpsico 4.0 questionnaire. The experimental results show that the vector support machine model has a performance level of 88.53%.

Año de publicación:

2020

Keywords:

• Urban bus driver
• Machine learning
• Psychosocial occupational risk

Fuente:

scopus