Detecting Mental Health with the Help of Machine Learning

The rising prevalence of mental health disorders globally presents an urgent need for innovative solutions to enhance early detection, accurate diagnosis, and effective intervention. Traditional methods of mental health assessment and diagnosis, which rely heavily on self-reported symptoms and clinical judgment, often fall short due to their subjectivity and the time-intensive nature of clinical evaluations. This thesis explores the potential of machine learning (ML) as a transformative tool in the field of mental health care, offering the promise of objective, scalable, and data-driven solutions. This thesis is structured to provide a comprehensive examination of the application of ML in detecting and predicting mental health disorders. The initial chapters offer a thorough literature review, detailing the current state of mental health diagnostics, the limitations of traditional methods, and the foundational concepts of machine learning. Following this, we delve into specific ML techniques, including supervised learning, unsupervised learning, deep learning, and natural language processing (NLP), each illustrated with relevant examples and case studies that demonstrate their applicability in mental health contexts.