• Dhanshree Deepak Shewale
• Kavita Uttam Sonawane
• Pranav Sanjay Palve
• Abhishek Ranjeet Mahato

The growing prevalence of mental health disorders calls for advanced, data-driven approaches to facilitate early detection and intervention. This paper proposes a novel deep learning-based technique to detect mental health conditions by analyzing user data, including behavioral patterns, social interactions, and personal communications. Unlike traditional diagnostic methods, this approach leverages modern data sources, such as social media posts, smartphone usage, and wearable sensor data, to develop a comprehensive profile of an individual’s mental well-being. Our deep learning model, incorporating natural language processing and sequential analysis techniques, identifies subtle emotional, cognitive, and behavioral cues associated with various mental health conditions. Preliminary results indicate that the proposed method achieves higher accuracy in early-stage detection than conventional diagnostic tools. The approach provides a scalable and non-invasive alternative, offering the potential for real-time monitoring and early intervention. Future work will explore extending this framework to diverse user populations and improving model robustness across various data inputs.

"A Noval Technique for Mental Health Detection Through User Data Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.b778-b781, November-2024, Available :http://www.jetir.org/papers/JETIR2411195.pdf

"A Noval Technique for Mental Health Detection Through User Data Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppb778-b781, November-2024, Available at : http://www.jetir.org/papers/JETIR2411195.pdf