Softcomputing approaches for detection of mental health

Abstract

We have simulated a dataset[21] using two most promising deep learning algorithms viz: Recurrent Neural Network (RNN) and Long short-term memory (LSTM). The accuracy reported by the RNN model is 0.78 whereas LSTM resulted in 0.82 accuracies. In conclusion, deep learning has the potential to provide early detection and treatment for mental health issues. However, further research is needed to improve the accuracy and reliability of these models and to evaluate their potential for widespread use in clinical settings. Deep learning techniques have shown great promise in the field of medical diagnosis, including the detection of mental health problems. This research aims to investigate the use of deep learning algorithms for the detection of mental health disorders, such as depression, anxiety and stress. The study will gather a large dataset of mental health-related data, including demographic information and self-reported symptoms. The data will then be processed and analyzed using deep learning algorithms, such as Convolutional Neural Networks and Recurrent Neural Networks, to build models that can accurately predict the presence of mental health disorders. The results of this research will contribute to the development of more efficient and effective mental health screening methods, which could greatly improve the early detection and treatment of mental health problems.