The Impact of Employees’ Mental Health Status on Performance Based on Data Mining

Authors

  • Feng He Humanities School, Jiaozuo University, Jiaozuo 454000, Henan, China

Abstract

Withtheintensification ofglobal competition, employee mental health issues have increasingly become akey factor affecting corporate performance. A large multinational technology company A has more than 20,000 employees worldwide. It was found that about 40% of employees had experienced varying degrees of mental health challenges, such as anxiety and depression, in the past year. To meet this challenge, Company A launched the “Psychological Capital Improvement Program", which aims to evaluate and improve employees’ mental health through data mining technology and psychological models, and improve job satisfaction and performance. The project team first conducted demand research and technology selection, chose suitable data mining tools, and established a multi-source data collection platform, integrating information such as mental health questionnaires, behavioral logs, and physiological signals. Through multiple linear regression, LSTM model and cluster analysis, the complex relationship between mental health status and performance was revealed. Based on these analysis results, personalized management strategies were formulated, such as interventions to stimulate work motivation and support mental health. Ultimately, through continuous monitoring and optimization of processes, Company A significantly improved the mental health level and work efficiency of its employees, while also promoting the growth of innovation capabilities. The success of this project not only brought significant economic benefits and social value to Company A, but also provided valuable experience for other companies in terms of mental health management.

Keywords: mental health management, data mining, personalized intervention, performance improvement, LSTM

Cite As

F. He, "The Impact of Employees’ Mental Health Status on Performance Based on Data Mining", 
Engineering Intelligent Systems, vol. 33 no. 5, pp. 597-606, 2025.

 

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Published

2025-09-01

Issue

Vol. 33 No. 5 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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