Publication

Abstract : The present study addresses the rising importance of mental health by devel oping a novel healthcare plan. We integrate physiological data from sensors, such as Heart Rate (HR) and Galvanic Skin Response (GSR), to predict and manage anxiety. These sensors provide non-invasive insights into the com plex relationship between physiological reactions and mental well-being. To analyze the collected data, we developed a novel algorithm, Regression Based Random Forest (RBRF). Using a large-scale dataset, we empirically validated the effectiveness of our approach, achieving an impressive 95 % accuracy in identifying anxiety. Our findings demonstrate the potential of sensor-based technologies and advanced algorithms to empower individuals to proactively monitor and manage their mental health. This approach holds significant promise for improving the precision and effectiveness of mental health care. • The study aims to improve mental healthcare by incorporating physiological data (Heart Rate and Galvanic Skin Response) to detect and potentially treat anxiety. • Employs a novel algorithm, Regression Based Random Forest (RBRF), to analyze the collected data and identify anxiety. • Achieved high accuracy (95 %) in identifying anxiety using the RBRF algorithm on a large dataset.