{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T02:25:24Z","timestamp":1772504724965,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>The increased prevalence of mental health issues in the workplace affects employees\u2019 well-being and organisational success, necessitating proactive interventions such as employee assistance programmes, stress management workshops, and tailored wellness initiatives. Artificial intelligence (AI) techniques are transforming mental health risk prediction using behavioural, environmental, and workplace data. However, the \u201cblack-box\u201d nature of many AI models hinders trust, transparency, and adoption in sensitive domains such as mental health. This study used the Open Sourcing Mental Illness (OSMI) secondary dataset (2016\u20132023) and applied four ML classifiers, Random Forest (RF), xGBoost, Support Vector Machine (SVM), and AdaBoost, to predict workplace mental health outcomes. Explainable AI (XAI) techniques, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), were integrated to provide both global (SHAP) and instance-level (LIME) interpretability. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance. The results show that xGBoost and RF achieved the highest cross-validation accuracy (94%), with xGBoost performing best overall (accuracy = 91%, ROC AUC = 90%), followed by RF (accuracy = 91%). SHAP revealed that sought_treatment, past_mh_disorder, and current_mh_disorder had the most significant positive impact on predictions, while LIME provided case-level explanations to support individualised interpretation. These findings show the importance of explainable ML models in informing timely, targeted interventions, such as improving access to mental health resources, promoting stigma-free workplaces, and supporting treatment-seeking behaviour, while ensuring the ethical and transparent integration of AI into workplace mental health management.<\/jats:p>","DOI":"10.3390\/informatics12040130","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T10:04:57Z","timestamp":1764151497000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Explainable Artificial Intelligence for Workplace Mental Health Prediction"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9046-4084","authenticated-orcid":false,"given":"Tsholofelo","family":"Mokheleli","sequence":"first","affiliation":[{"name":"Department of Applied Information Systems, University of Johannesburg, Johannesburg 2006, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3710-2513","authenticated-orcid":false,"given":"Tebogo","family":"Bokaba","sequence":"additional","affiliation":[{"name":"Department of Applied Information Systems, University of Johannesburg, Johannesburg 2006, South Africa"}]},{"given":"Elliot","family":"Mbunge","sequence":"additional","affiliation":[{"name":"Department of Applied Information Systems, University of Johannesburg, Johannesburg 2006, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15761\/PMCH.1000104","article-title":"Mental health is an integral part of the sustainable development goals","volume":"1","author":"Dybdahl","year":"2018","journal-title":"Prev. Med. Community Health"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"LaMontagne, A.D., Martin, A., Page, K.M., Reavley, N.J., Noblet, A.J., Milner, A.J., Keegel, T., and Smith, P.M. (2014). Workplace mental health: Developing an integrated intervention approach. BMC Psychiatry, 14.","DOI":"10.1186\/1471-244X-14-131"},{"key":"ref_3","unstructured":"World Health Organization (2022). WHO Guidelines on Mental Health at Work, World Health Organization."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1007\/s10198-021-01379-w","article-title":"The effect of mental and physical health problems on sickness absence","volume":"22","author":"Bryan","year":"2021","journal-title":"Eur. J. Health Econ."},{"key":"ref_5","unstructured":"Mokheleli, T. (2024, October 29). A Comparison of Machine Learning Techniques for Predicting Mental Health Disorders. Available online: https:\/\/hdl.handle.net\/10210\/511142."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1080\/10503307.2022.2138792","article-title":"The Dodo Bird and the need for scalable interventions in global mental health\u2014A commentary on the 25th anniversary of Wampold et al. (1997)","volume":"33","author":"Cuijpers","year":"2023","journal-title":"Psychother. Res."},{"key":"ref_7","unstructured":"Peykar, P. (2024, December 09). The Relationship Between Servant Leadership, Supportive Work Environment, and Tech Employees\u2019 Mental Well-Being. Available online: https:\/\/digitalcommons.liberty.edu\/doctoral\/6027."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bhavya, C. (2025). Impact of Mental Health Initiatives on Employee Productivity. Int. J. Sci. Res. Eng. Manag., Available online: https:\/\/doi.org\/10.55041\/IJSREM46519.","DOI":"10.55041\/IJSREM46519"},{"key":"ref_9","first-page":"44","article-title":"Mental Health Matters: How Companies Are Failing Their Employees","volume":"8","author":"Celestin","year":"2024","journal-title":"SSRN Electron. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"\u00d6zer, G., and Escart\u00edn, J. (2024). Imbalance between Employees and the Organisational Context: A Catalyst for Workplace Bullying Behaviours in Both Targets and Perpetrators. Behav. Sci., 14.","DOI":"10.3390\/bs14090751"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ypmed.2017.03.017","article-title":"Workplace mental health: An international review of guidelines","volume":"101","author":"Memish","year":"2017","journal-title":"Prev. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e1097","DOI":"10.5435\/JAAOS-D-19-00822","article-title":"Harassment, Discrimination, and Bullying in Orthopaedics: A Work Environment and Culture Survey","volume":"28","author":"Weber","year":"2020","journal-title":"J. Am. Acad. Orthop. Surg."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lohia, A., Ranjith, A., Sachdeva, A., and Bhat, A. (2023, January 17\u201319). An Ensemble Technique to Analyse Mental Health of Workforce in the Corporate Sector. Proceedings of the 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS56967.2023.10142470"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1007\/s11920-019-1094-0","article-title":"Artificial Intelligence for Mental Health and Mental Illnesses: An Overview","volume":"21","author":"Graham","year":"2019","journal-title":"Curr. Psychiatry Rep."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Iyortsuun, N.K., Kim, S.-H., Jhon, M., Yang, H.-J., and Pant, S. (2023). A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis. Healthcare, 11.","DOI":"10.3390\/healthcare11030285"},{"key":"ref_16","unstructured":"Mokheleli, T., Bokaba, T., and Museba, T. (2025, July 12). An In-Depth Comparative Analysis of Machine Learning Techniques for Addressing Class Imbalance in Mental Health Prediction. Available online: https:\/\/aisel.aisnet.org\/acis2023\/15."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Almaleh, A. (2023, January 13\u201315). Machine Learning-Based Forecasting of Mental Health Issues Among Employees in the Workplace. Proceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), Bali, Indonesia.","DOI":"10.1109\/IAICT59002.2023.10205620"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chauhan, T., and Renjith, P. (2024, January 24\u201325). Utilizing Machine Learning to foster employee mental health in modern workplace environment. Proceedings of the 2024 IEEE International Students\u2019 Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India.","DOI":"10.1109\/SCEECS61402.2024.10482079"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s44163-024-00114-7","article-title":"Explainable and interpretable artificial intelligence in medicine: A systematic bibliometric review","volume":"4","author":"Frasca","year":"2024","journal-title":"Discov. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"121560","DOI":"10.1016\/j.techfore.2022.121560","article-title":"Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic","volume":"178","author":"Nanath","year":"2022","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1038\/s41746-023-00751-9","article-title":"Explainable artificial intelligence for mental health through transparency and interpretability for understandability","volume":"6","author":"Joyce","year":"2023","journal-title":"npj Digit. Med."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rautaray, S.S., Nayak, S., and Pandey, M. (2023, January 15\u201317). A Machine Learning Based Employee Mental Health Analysis Model. Proceedings of the 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India.","DOI":"10.1109\/ICSCNA58489.2023.10370378"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1007\/s10916-018-0934-5","article-title":"Behavioral Modeling for Mental Health using Machine Learning Algorithms","volume":"42","author":"Srividya","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mallick, S., and Panda, M. (2024, January 5\u20137). Predictive Modeling of Mental Illness in Technical Workplace: A Feature Selection and Classification Approach. Proceedings of the 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0, Raigarh, India.","DOI":"10.1109\/OTCON60325.2024.10688102"},{"key":"ref_25","unstructured":"Sujal, B., Neelima, K., Deepanjali, C., Bhuvanashree, P., Duraipandian, K., Rajan, S., and Sathiyanarayanan, M. (2022, January 4\u20138). Mental Health Analysis of Employees using Machine Learning Techniques. Proceedings of the 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), Bangalore, India."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kapoor, A., and Goel, S. (2023, January 6\u20138). Predicting Stress at Workplace using Machine Learning Techniques. Proceedings of the 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India.","DOI":"10.1109\/ICCCNT56998.2023.10306720"},{"key":"ref_27","first-page":"1","article-title":"Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges","volume":"2022","author":"Chung","year":"2022","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S. (2020). Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy, 23.","DOI":"10.3390\/e23010018"},{"key":"ref_29","unstructured":"Zilker, S., Weinzierl, S., Zschech, P., Kraus, M., and Matzner, M. (2023, January 11\u201316). Best of Both Worlds: Combining Predictive Power with Interpretable and Explainable Results for Patient Pathway Prediction. Proceedings of the ECIS 2023, Kristiansand, Norway. Available online: https:\/\/aisel.aisnet.org\/ecis2023_rp\/266."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dwivedi, K., Rajpal, A., Rajpal, S., Kumar, V., Agarwal, M., and Kumar, N. (2024). Enlightening the path to NSCLC biomarkers: Utilizing the power of XAI-guided deep learning. Comput. Methods Programs Biomed., 243.","DOI":"10.1016\/j.cmpb.2023.107864"},{"key":"ref_31","unstructured":"Letoffe, O., Huang, X., Asher, N., and Marques-Silva, J. (2024). From SHAP Scores to Feature Importance Scores. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101587","DOI":"10.1016\/j.imu.2024.101587","article-title":"A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges","volume":"51","author":"Mienye","year":"2024","journal-title":"Inform. Med. Unlocked."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e70018","DOI":"10.1002\/widm.70018","article-title":"Unveiling Explainable AI in Healthcare: Current Trends, Challenges, and Future Directions","volume":"15","author":"Noor","year":"2025","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.aej.2024.12.120","article-title":"Explainable artificial intelligence systems for predicting mental health problems in autistics","volume":"117","author":"Atlam","year":"2025","journal-title":"Alex. Eng. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e72038","DOI":"10.2196\/72038","article-title":"Explainable AI for Depression Detection and Severity Classification From Activity Data: Development and Evaluation Study of an Interpretable Framework","volume":"12","author":"Ahmed","year":"2025","journal-title":"JMIR Ment. Health"},{"key":"ref_36","unstructured":"(2024, December 01). OSMI: About, O.S.M.I. Available online: https:\/\/osmihelp.org\/about\/about-osmi.html."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1146\/annurev-orgpsych-120920-050527","article-title":"Mental Health in the Workplace","volume":"10","author":"Kelloway","year":"2023","journal-title":"Annu. Rev. Organ. Psychol. Organ. Behav."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"759","DOI":"10.4236\/ojs.2021.115045","article-title":"Why Can Multiple Imputations and How (MICE) Algorithm Work?","volume":"11","author":"Alruhaymi","year":"2021","journal-title":"Open J. Stat."},{"key":"ref_39","unstructured":"International Labour Organization (2018). ILO Convention No. 138 at a Glance, International Labour Organization. Available online: https:\/\/www.ilo.org\/publications\/ilo-convention-no-138-glance."},{"key":"ref_40","first-page":"331","article-title":"Investigating the Impact of Train\/Test Split Ratio on the Performance of Pre-Trained Models with Custom Datasets","volume":"15","author":"Bichri","year":"2024","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Komorowski, M., Marshall, D.C., Salciccioli, J.D., and Crutain, Y. (2016). Exploratory Data Analysis. Secondary Analysis of Electronic Health Records, Springer International Publishing.","DOI":"10.1007\/978-3-319-43742-2_15"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Alghamdi, M., Al-Mallah, M., Keteyian, S., Brawner, C., Ehrman, J., and Sakr, S. (2017). Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford Exercise Testing (FIT) project. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0179805"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1007\/s13204-021-02063-4","article-title":"Effective treatment of imbalanced datasets in health care using modified SMOTE coupled with stacked deep learning algorithms","volume":"13","author":"Sowjanya","year":"2023","journal-title":"Appl. Nanosci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"100185","DOI":"10.1016\/j.health.2023.100185","article-title":"A hybrid mental health prediction model using Support Vector Machine, Multilayer Perceptron, and Random Forest algorithms","volume":"3","author":"Mohamed","year":"2023","journal-title":"Healthc. Anal."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. arXiv.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"106957","DOI":"10.1016\/j.asoc.2020.106957","article-title":"Intelligent modeling strategies for forecasting air quality time series: A review","volume":"102","author":"Liu","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"120253","DOI":"10.1016\/j.neuroimage.2023.120253","article-title":"Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data","volume":"277","author":"Abdelhedi","year":"2023","journal-title":"Neuroimage"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Padilla, R., Netto, S.L., and da Silva, E.A.B. (2020, January 1\u20133). A Survey on Performance Metrics for Object-Detection Algorithms. Proceedings of the 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Niter\u00f3i, Brazil.","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3097","DOI":"10.1007\/s11063-022-10756-2","article-title":"Cost-Sensitive Learning based on Performance Metric for Imbalanced Data","volume":"54","author":"Aurelio","year":"2022","journal-title":"Neural Process Lett."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ma, X., Hou, M., Zhan, J., and Liu, Z. (2023). Interpretable Predictive Modeling of Tight Gas Well Productivity with SHAP and LIME Techniques. Energies, 16.","DOI":"10.3390\/en16093653"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TKDE.2019.2912815","article-title":"Reliable Accuracy Estimates from k-Fold Cross Validation","volume":"32","author":"Wong","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Marcilio, W.E., and Eler, D.M. (2020, January 7\u201310). From explanations to feature selection: Assessing SHAP values as feature selection mechanism. Proceedings of the 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Recife\/Porto de Galinhas, Brazil.","DOI":"10.1109\/SIBGRAPI51738.2020.00053"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e70056","DOI":"10.1111\/cts.70056","article-title":"Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development","volume":"17","author":"Schmitt","year":"2024","journal-title":"Clin. Transl. Sci."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T10:51:21Z","timestamp":1764154281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":53,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["informatics12040130"],"URL":"https:\/\/doi.org\/10.3390\/informatics12040130","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]}}}