Key result
The implementation of machine learning algorithms using physiological data successfully identified stress and anxiety levels among workers of a public mobility company.
Why the study?
High levels of stress and anxiety were present among workers of the Empresa Pública de Movilidad del Norte.
Cross-Sectional (n=62)
No
Machine learning algorithms using physiological data can help identify stress and anxiety levels in employees to improve occupational health.
ML-based stress/anxiety screening feasible in occupational cohorts; leaves open validation for cardiovascular risk prediction.
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
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Vásquez et al. (2024) conducted a cross-sectional in Stress and anxiety (n=62). Machine learning algorithms for stress and anxiety detection was evaluated on Identification of stress and anxiety levels. The implementation of machine learning algorithms using physiological data successfully identified stress and anxiety levels among workers of a public mobility company.
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