A Comparative Study of Supervised and Unsupervised Machine Learning Algorithms for Stress Detection

Abstract

Stress detection has become an increasingly important area of research, with applications spanning healthcare, occupational safety, and mental health. Machine learning (ML) algorithms have shown considerable potential in identifying stress from various data sources such as physiological signals, voice recordings, and even text. This review paper explores and compares the two primary categories of machine learning algorithms supervised and unsupervised for stress detection. We systematically review existing studies, discuss their methodologies, and analyze the strengths and weaknesses of each approach. The goal is to provide insights into the most effective methods for automatic stress detection and highlight the challenges and opportunities for future research.