Enhancing the Surveillance, Assessment, and Tracking of Mental Health and Well-being Using Deep Learning Techniques

Mental Health and well-being among people can be crucial concerns, thus making it important for productive models or tools to detect the issue and monitor the situation. We proposed a Convolutional Network Model(CNN), as the ideal solution for examining the data of mental health, by evaluating numerous deep-learning models. Earlier, we worked on exploring Recurrent Neural Networks(RNN) and Gated Recurrent Units(GRU). The CNN model signified a high performance making it the ideal model, by achieving high accuracy. The ability of CNN to successfully consider complex, and various multi-dimensional patterns from the data, makes it the appropriate model for this task.The precision and robustness of this model, call attention to its capability to intensify mental health assessment and tracking systems. The CNN approach not only enhances the accuracy of detection but also comes up with an automated solution for monitoring mental health, with notable inferences for implementation in many other domains like educational, clinical, and community settings. The study highlights the significance of the selection of an ideal deep-learning model to expand the productiveness of mental health interferences in people.