AUTHOR=Alumran Arwa , Alshahrani Bashayer , Aslam Nida , Khan Irfan Ullah , AlShedayed Rana , AlFrayan Dina , AlEssa Rand , Mirza Samiha , Alshakhs Fatima 

TITLE=Explainable machine learning for predicting hospital employees' quality of life using psychosocial work environment data

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1529802

DOI=10.3389/fpubh.2025.1529802

ISSN=2296-2565

ABSTRACT=Health-Related Quality of Life (HRQL) embodies the impact of an individual's health on their ability to live a fulfilling life. Quality of Life (QoL) is influenced by a range of factors, including physical functioning and wellbeing, psychological functioning, work environment (WE), lifestyle, and social relations. Various studies have found that job-related factors can be an essential predictor of an individual's HRQL. Furthermore, the Psychosocial Work Environment (PWE) can affect workers' wellbeing and contribute to the company's sustainability. PWE and QoL influence the quality of health services provided by healthcare providers. Therefore, the relationships among QoL, PWE, and healthcare quality need to be assessed to identify factors that improve overall patient healthcare service quality. This relationship has not been extensively evaluated in the Saudi context. Therefore, in the current study, we aimed to employ machine learning (ML) techniques to predict employee QoL using PWE data from a hospital in the Kingdom of Saudi Arabia (KSA). Several ML models have been developed to predict HRQL effectively and their significant attributes; the experiments were carried out with and without feature engineering. The Naïve Bayes (NB) classifier achieved the highest precision of 1.0 (95% CI: 0.81–1.0) in predicting employees' QoL using PWE and demographic variables. The selected Work Environment (WE) features, identified using the Xverse voting selector with the SVM classifier achieved the best results, with accuracy, recall, precision, F1, and receiver operating characteristic (ROC) reaching 0.92 (95% CI: 0.88–0.95), 0.90 (95% CI: 0.86–0.98), 0.95 (95% CI: 0.86–0.99), 0.92 (95% CI: 0.88–0.95), and 0.9, respectively. Post-hoc Explainable Artificial Intelligence (XAI) was used to alleviate the black-box nature of SVM and add transparency to the model. In conclusion, this study provides a robust, explainable tool for predicting employee QoL that can help healthcare organizations improve quality.