Happy work: Improving enterprise human resource management by predicting workers’ stress using deep learning

Yu Zhang and Ershi Qi

PLOS ONE, 2022, vol. 17, issue 4, 1-18

Abstract: Recently, workers in most enterprises suffer from excessive occupational stress in the workplace, which negatively affects workers’ productivity, safety, and health. To deal with stress in workers, it is vital for the human resource management (HRM) department to manage stress effectively, bridging the gap between management and stressed employees. To manage stress effectively, the first step is to predict workers’ stress and detect the factors causing stress among workers. Existing methods often rely on the stress assessment questionnaire, which may not be effective to predict workers’ stress, due to 1) the difficulty of collecting the questionnaire data, and 2) the bias brought by workers’ subjectivity when completing the questionnaires. In this paper, we aim to address this issue and accurately predict workers’ stress status based on Deep Learning (DL) approach. We develop two stress prediction models (i.e., stress classification model and stress regression model) and correspondingly design two neural network architectures. We train these two stress prediction models based on workers’ data (e.g., salary, working time, KPI). By conducting experiments over two real-world datasets: ESI and HAJP, we validate that our proposed deep learning-based approach can effectively predict workers’ stress status with 71.2% accuracy in the classification model and 11.1 prediction loss in the regression model. By accurately predicting workers’ stress status with our method, the HRM of enterprises can be improved.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0266373

DOI: 10.1371/journal.pone.0266373

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