Early Detection and Prediction of Mental Health Disorders through Advanced Deep Learning Techniques

Abstract

Delays in diagnosing mental health issues can have serious clinical and societal repercussions, making them a major worldwide health concern. In the early detection, diagnosis, and treatment of mental diseases and disorders, deep learning (DL) as well as machine learning (ML) have started having a significant role. These technologies have a chance to greatly enhance treatment results by analysing complicated data from genetic, imaging, and behavioural assessments. They do, however, also provide particular difficulties with regard to integrating data and moral dilemmas. The majority of conventional diagnostic techniques rely on clinical assessments and self-reported symptoms, these can be imprecise and insufficient for early identification. By examining intricate and high-dimensional data patterns, this research offers a sophisticated deep learning-based framework for the early identification and predicted mental health issues. Robust detection of emotional risk indicators is made possible by the suggested method, which uses architectures of deep neural networks to continually acquire discriminative features from multisensory data sources. In terms of accuracy in forecasting, sensitivity, and generalisation capacity, experimental evaluation shows that the deep learning technologies outperform conventional machine learning techniques. The findings demonstrate how cutting-edge deep learning methods can help with clinical decision-making, improve early diagnosis, and boost mental health outcomes. This study emphasises how intelligent, data-driven platforms may improve predictive healthcare and mental health monitoring.