Detecting Mental Stress Using Ensemble Machine Learning Methods

Abstract

Mental stress is a prevalent issue affecting individuals' well-being and productivity. Accurate detection and monitoring of mental stress can lead to timely interventions and improved mental health outcomes. This study presents a novel approach to mental stress detection by leveraging ensemble machine learning methods. By integrating multiple machine learning algorithms, the proposed ensemble model enhances prediction accuracy and reliability. The effectiveness of the ensemble model is evaluated using physiological and behavioral data collected from participants. Results indicate that the ensemble method outperforms individual machine learning models in detecting mental stress, offering a robust solution for real-world applications.