EMOTIONAL ILLNESS DETECTION USING DEEP LEARNING

Abstract

A substantial societal concern posed by the increased incidence of mental diseases has prompted research into cutting-edge technology to improve forecasting capacities and guide targeted interventions. Deep Learning (DL), a type of machine learning, has drawn a lot of attention for its potential to forecast and categorize the outcomes of mental illness using a variety of data sources. This systematic review summarizes prior work on the use of deep learning techniques to forecast the onset of mental illness. This review explains the benefits and drawbacks of current methodologies while seeing patterns and promising directions for further investigation. It does this by looking at a variety of datasets, model architectures, and evaluation criteria. In addition to underlining the necessity for standardized procedures, improved interpretability, and ethical issues when deploying these predictive models in actual healthcare settings, the synthesis of findings highlights the promising potential of deep learning in predicting mental illness. This review offers insightful guidance for the improvement and growth of deep learning-based prediction models, enabling eventually more efficient and individualized approaches to mental illness prevention and treatment as mental health interventions continue to advance.