A Deep learning approach for detecting and mitigating mental health conditions through a user-centred interactive interface

Abstract

Mental health disorders, such as depression, anxiety, attention deficit hyperactivity disorder (ADHD), bipolar disorder, personality disorder (PD), and post-traumatic stress disorder (PTSD), are growing global concerns that affect millions of individuals across all age groups and backgrounds. These conditions not only impair emotional and psychological well-being, but also hinder social relationships, academic performance, and professional productivity [1]. Deep neural networks have achieved high accuracy in detecting conditions from social media text and other user-generated content [2]. This research focuses on developing an AI-driven mental health framework comprising a user-centred interactive interface for intuitive engagement, a deep learning (DL) model for mental health status detection, and a knowledge-based recommendation system (KBRS) for adaptive, personalized interventions. With the DL model successfully developed and validated, the next phase focuses on implementing the usercentred interface and enhancing the KBRS.