A Comprehensive Overview of Machine Learning Techniques in Predicting Mental Stress

Abstract

Mental stress has become a pervasive global health challenge with significant physiological and psychological implications. This comprehensive research paper explores the potential of machine learning techniques in accurately predicting and understanding mental stress using advanced data analysis methodologies. By leveraging smart watch technologies and sophisticated computational models, various machine learning algorithms for stress detection and prediction have been investigated in this paper. Our study examines multiple predictive models, including random forests, decision trees, support vector machines, naive Bayes, logistic regression, and k-nearest neighbor approaches, applied to physiological data collected through wearable devices. Through rigorous k-fold cross-validation and voting ensemble learning techniques, we analyze the effectiveness of these algorithms in identifying stress indicators. The research highlights the support vector machine model's exceptional performance, achieving a remarkable 94% binary class stress prediction accuracy. The findings underscore the transformative potential of machine learning in mental health diagnostics, offering a non-invasive, data-driven approach to early stress detection.