Predicting Stress, Anxiety, and Depression: A Multi-Modal Approach

With respect to research, our focus addresses the streamed and growing area of the effects of common mental health conditions such as stress, anxiety, and depression on people and societal wellbeing. Our study has designed a multi layered framework, using deep learning, machine learning and natural language processing to build a set of generalized predictive models. These models are capable of finding early indications of mental health problems, which include; The models consist of various neural networks such as CNNs, LSTMs, BiLSTMs, GRUs, and dense models alongside ML algorithms such as the Random Forest and Support Vector Machine. Analyzing social media content with the help of NLP, we got more detailed views on all the factors influencing mental health. It highlights the necessity of the simple and accurate mental health prediction to face the rates of these disorders by underlining it. The research on multiple model structures, formulae, and the integration of NLP to coordinate with the social media analysis attempts to construct the implemented solution for the prevention and awareness of mental health issues. This minimally invasive approach is meant to provide community members as well as individuals with more appropriate mental health services during early intervention and prevention.