AI-Driven Mental Health Monitoring System: A Predictive Framework for Anxiety, Depression, and Stress Management
Mr. Rajesh Sharma, Ayushi Saini, Mansi Yadav, Ravi Singh
Master of Computer Applications
Galgotias University
Abstract
Mental well-being plays a vi- tal role in maintaining an individ- ual’s overall health, influencing emo- tional stability, cognitive function, and social interactions. Over the past few years, mental health is- sues such as anxiety, depression, and chronic stress have become increas- ingly prevalent, posing significant global health challenges. Barriers such as social stigma, limited access to mental health services, and the subjectivity of conventional diagno- sis methods often hinder early inter- vention and support.
To address these limitations, this research proposes a novel AI- powered mental health monitoring system. The system utilizes machine learning techniques to process and analyze multimodal data—ranging from physiological signals (like sleep duration, heart rate, and physical activity) to digital behavior (including social media engagement and textual expressions). Through feature engineering, the system generates high-level indicators such as Routine Disruption Index, Mood-Stress Balance, and Social Engagement Scores, helping quantify complex emotional states.
Using both regression and classifi- cation models, the system effectively predicts mental health metrics such as anxiety levels, depression scores, and high-stress alerts. This allows for continuous, real-time monitoring and supports early identification of potential mental health issues.
Beyond technical accuracy, the system incorporates ethical safe- guards, emphasizing data privacy, user transparency, and responsible AI use. It also aligns with the United Nations’ Sustainable Devel- opment Goal (SDG) 3 — “Ensure healthy lives and promote well-being for all at all ages” — by providing a scalable and technology-driven ap- proach to mental health care.
Extensive validation demonstrates that the proposed system delivers strong predictive performance across varied datasets. This work showcases the transformative potential of artifi- cial intelligence in enhancing mental health support and building more ac- cessible, data-informed solutions for psychological well-being.
