Machine Learning Technique Enabled Learning Methodology for Human Depression Prediction

Human Depression Prediction is important for a number of reasons, the most important of which are that it can help with faster interventions and better mental health outcomes. First and foremost, early identification of depression facilitates timely and focused interventions, allowing patients to obtain the right care and support prior to the worsening of their illness. By combining the deep learning powers of the LeNet architecture with the optimization skills of the Grey Wolf Optimization (GWO) algorithm, the Human Depression Prediction Scheme (HDPS) presents a novel approach to depression prediction. The proposed technique shows promise in identifying depression tendencies with a noteworthy 95% accuracy rate.Notably, Google Colab uses the HDPS, highlighting its usability and accessibility. By combining cutting-edge technologies and optimization strategies, HDPS is positioned as a potentially useful tool for depression early diagnosis, providing high accuracy and useful application in a computing environment that is widely available.