Enhancing Mental Health Care: A Proposed Approach for Depression Detection using CNN and LSTM Model

In the landscape of mental health research, the detection of depression from textual content has gained paramount importance, promising crucial early intervention for those struggling with their mental health. This paper introduces an innovative LSTM-CNN model, meticulously crafted to analyze social media posts, online discussions, and blogs, showcasing a remarkable 93.41% accuracy in depression detection. This model, proficient in capturing both sequential intricacies and localized patterns, surpasses existing classifiers, firmly establishing its efficacy. This research marks a significant advancement in mental health technology, illuminating the potential of sophisticated machine learning architectures in revolutionizing precise and dependable depression detection. In an era where mental health support is paramount, this study heralds a promising future where cutting-edge technologies redefine our approach, ensuring timely help for individuals battling depression.