Description

Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year. Potential depression sufferers usually do not consult psychological doctors because they feel ashamed or are unaware of any depression, which may result in severe delay of diagnosis and treatment. In the meantime, evidence shows that social media data provides valuable clues about physical and mental health conditions. Automated detection methods can help identify depressed or individuals at risk. The objective is to improve depression detection performance by examining: hybrid method. The results show that hybrid models classification results. We are implementing machine learning, deep learning and hybridation model logistic regression, artificial neural network and hybrid give the high accuracy with prediction result.