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Employee Mental Workload Classification in Industrial Workplaces: A Machine Learning Approach

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Advances in Intelligent Computing Techniques and Applications (IRICT 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 211))

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Abstract

Employees at industrial workplaces are expected to produce labour of a certain standard. They are instructed to improve their quality of work, and this may take a toll on their mental health. Mental workload directly affects employees’ performance, productivity, and well-being. Therefore, this paper conducts a comparative study for the classification of mental workload where a mental workload dataset is subjected to four machine learning classification models-Naïve Bayes, Extreme Gradient Boosting, Support Vector Machine and K-Nearest Neighbour. Their performance is measured against the performance metrics-accuracy, precision, recall and f1-score. Before synthetic minority oversampling method Support Vector Machine performed the best with 90.41% accuracy and K-Nearest Neighbour performed the best with 98.61% accuracy after Synthetic Method of Oversampling Technique.

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Acknowledgement

The authors appreciate Universiti Sains Malaysia for supporting this study from RUTeam grant (1001.PKOMP.8580093).

Author information

Authors and Affiliations

  1. School of Computer Science, Universiti Sains Malaysia, 11800, Penang, Malaysia

    Ayesha Hussain

  2. Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland

    Pantea Keikhosrokiani

  3. Faculty of Medicine, University of Oulu, Oulu, Finland

    Pantea Keikhosrokiani

  4. Faculty of Humanities, University of Oulu, Oulu, Finland

    Moussa Pourya Asl

Authors
  1. Ayesha Hussain
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  2. Pantea Keikhosrokiani
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  3. Moussa Pourya Asl
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Corresponding author

Correspondence to Pantea Keikhosrokiani .

Editor information

Editors and Affiliations

  1. School of Computing and Digital Techn., Birmingham City University, BIRMINGHAM, UK

    Faisal Saeed

  2. Department of Business Analytics, Sunway Business School, Sunway University, Selangor, Malaysia

    Fathey Mohammed

  3. Department of Computer Sciences and Electrical Engineering, Marshall University, Huntington, WV, USA

    Yousef Fazea

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Hussain, A., Keikhosrokiani, P., Asl, M.P. (2024). Employee Mental Workload Classification in Industrial Workplaces: A Machine Learning Approach. In: Saeed, F., Mohammed, F., Fazea, Y. (eds) Advances in Intelligent Computing Techniques and Applications. IRICT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-031-59707-7_4

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