Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource John, Juswin Sajan; George, Shiju; Sharon Roji Priya, C. Title A name given to the resource Workplace Stress Prediction Using Explainable AI: A Non-intrusive Approach with Psychosocial and Demographic Features Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1629 LNNS;pp.334-347 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-05548-4_27" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-05548-4_27</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019526527?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019526527?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Explainable AI; Machine Learning/Deep Learning; Non-Intrusive Approach; Occupational Stress; Psychosocial Stressors; Workplace Stress Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303205547-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online