Detecting an Employee’s Stress Using Emotional Intelligence-Based Machine Learning Models

Stress among employees is a crucial element that has a big impact on output and general well-being at work. Maintaining a productive and healthy workforce requires early intervention in the form of stress level identification and management. This study offers a machine learning method that uses emotional intelligence algorithms to identify employees’ stress levels and determine which ones would benefit from therapy for stress management. Through the integration of sophisticated machine learning algorithms with emotional intelligence measurements, the suggested model provides a reliable framework for evaluating employee stress in real-time. A large dataset is used to assess the model’s efficacy, demonstrating how well it predicts stress-related problems and suggests relevant counseling actions. The results show how this strategy has the ability to promote employee well-being and, as a result, enhance organizational performance through proactive.