Implementation of machine learning algorithms to identify stress and anxiety levels in the personnel of a northern public mobility company

This scientific article analyzes the use of machine learning techniques to identify levels of stress and anxiety among workers of the Empresa Pública de Movilidad del Norte. The problem lies in the high levels of stress and anxiety present in the staff. The company, committed to the prevention of occupational risks and the physical and mental well-being of its employees, implemented a control system based on an analytical-synthetic approach. These techniques processed data, identified patterns and improved their accuracy with each analysis. Through interviews with the manager, head of Human Resources and occupational physician, along with employee surveys, stress control and quality of work life were improved