A Hybrid Knowledge Graph-Deep Learning Model for Predictive Analytics in Healthcare Worker Stress Management with Cloud Environments

Healthcare professionals experience severe psychological strain from difficult schedules and increased patient volumes, resulting in burnout and minimised efficiency. Conventional stress monitoring methods lack real-time predictive functionality and do not involve structured domain knowledge. This study presents a Hybrid Knowledge Graph-Deep Learning Model for predictive analytics in stress management, utilising graph embeddings and deep learning frameworks to improve interpretability. The solution integrates cloud-based deployment, facilitating real-time inference and scalability. The suggested system attains 96.4% accuracy, 95.8% F1-score, and a mean squared error of 0.021, more than the performance of Random Forest, SVM, and CNN models. The introduction of Knowledge Graph embeddings enhances contextual awareness, whereas deep learning guarantees adaptability across various healthcare settings. The results indicate improved accuracy in stress prediction, real-time monitoring capabilities, and greater explainability, positioning this method as an important boost for proactive stress management strategy in healthcare environments.