Mental Health Tracking and Prediction for Stressed Working Professionals

Stress is a common mood disorder affecting many people worldwide, with its prevalence on the rise. While our bodies can adapt to stress and change, it becomes problematic when we don't have time to relax. To address this, researchers have explored various methods for tracking emotions and identifying stress. This study focuses on detecting stress, anxiety, depression, as well as other disorders like sleep disorders and PTSD, using machine learning algorithms. The research evaluates 24 and 10 machine learning algorithms on two datasets: Human Stress Prediction (HSP) and Mental Health in Tech (MHT), respectively. The findings reveal average testing accuracies of 77.042% and 83.597% for the HSP and MHT datasets, respectively.