Innovating workplace mental health strategies with advanced machine learning: application of a superior ensemble classifier for accurate stress detection in business environments

This study introduces a novel approach leveraging cutting-edge ensemble classification algorithms to tackle the persistent issue of job stress among corporate professionals. Addressing the urgent need for effective stress-reduction strategies, our approach integrates the strengths of multiple base classifiers, including random forest, multi-layer perceptron (MLP), decision tree, and K-nearest neighbors (KNN). By combining these classifiers into a hybrid ensemble model, BG_ensemble, we achieve an unprecedented accuracy rate of 95.34%, surpassing the performance of individual classifiers. This exceptional accuracy underscores the effectiveness of our ensemble technique in identifying and managing workplace stress. Beyond its technical prowess, our method provides a comprehensive framework for proactive stress management, incorporating precision and recall metrics to ensure reliable detection and intervention. Implementing this ensemble approach promises significant benefits for enhancing the well-being of corporate professionals and fostering a more supportive work environment. Our research represents a substantial advancement in advancing resilience and mental health awareness within the corporate sector, transforming stress detection and intervention practices. This study is a crucial step toward addressing the pervasive issue of workplace stress and promoting a culture of well-being and productivity, with the potential to contribute to a more resilient and sustainable work environment.