ItemConference Paper

Workplace Stress Prediction Using Explainable AI: A Non-intrusive Approach with Psychosocial and Demographic Features

Title
Workplace Stress Prediction Using Explainable AI: A Non-intrusive Approach with Psychosocial and Demographic Features
Creator
John, Juswin Sajan; George, Shiju; Sharon Roji Priya, C.
Description
This study investigates non-intrusive approaches to workplace stress detection using machine learning/deep learning techniques. Current methods rely heavily on physiological measurements (ECG, EDA, PPG) or behavioral monitoring which face implementation challenges in corporate environments. We propose an alternative approach utilizing psychosocial stressors and demographic variables to develop an explainable AI model for occupational stress detection. This approach addresses the practical limitations of current methods, which fail to discover underlying contributors to employee stress in the workplace. Two datasets were evaluated: The corporate stress dataset (CSDIW) with 50,000 records across 30 features and the HR Analytics Job Prediction dataset (HAJP) with 15,000 records across 10 features. Machine learning models including Logistic Regression, Nae Bayes, Neural Networks, Autoencoders?+?XGBoost and ensemble methods (SVM, RF and XGBoost) were implemented in different feature spaces. Results indicate improved performance on HAJP dataset with and without feature engineering whereas models consistently underperformed on the CSDIW dataset. The soft voting-based ensemble classifier turned out the best performer achieving an accuracy of 0.86 and an f1 score of 0.84. Our findings suggest that while psychosocial features hold promise for non-intrusive stress detection in workplace, data quality and appropriate labelling remain critical challenges. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Source
Lecture Notes in Networks and Systems;Volume;1629 LNNS;pp.334-347
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Explainable AI; Machine Learning/Deep Learning; Non-Intrusive Approach; Occupational Stress; Psychosocial Stressors; Workplace Stress
Coverage
John J.S., Department of AI, ML and Data Science, Christ University, Karnataka, Bengaluru, 560074, India; George S., Department of AI, ML and Data Science, Christ University, Karnataka, Bengaluru, 560074, India; Sharon Roji Priya C., Department of Computer Science and Engineering, Christ University, Karnataka, Bengaluru, 560074, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-303205547-7;
Format
online
Language
English
Type
Conference paper

Citation

John, Juswin Sajan; George, Shiju; Sharon Roji Priya, C., “Workplace Stress Prediction Using Explainable AI: A Non-intrusive Approach with Psychosocial and Demographic Features,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25355.

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