Depression Detection Using Machine learning

Abstract - According to the World Health organization, by2030depression will be the second leading cause of disable Depression is state of Mental illness. It is characterized by long lasting feeling of sadness or despair. Most patients with depression do not complain that they are depressed. If a person is sad for really long time, then that person can be considered as depressed person. such person needs to take the proper diagnosis with the help of psychiatrist. But this people did not like visit the psychiatrist to check that if they are depressed or not because they are fear people judgements. In recent years machine learning (ML) Has emerged as a powerful tool for the automatic detection of depression by analyzing patterns in behavioral and physiological data. this research explore, the application of ML algorithms to identify depressive symptoms using data derived from source such as social media post, speech signals and structure questionaries. We evaluate multiple classify including support vector machines. (SVM), Random Forest and deep learning models to determine their effectiveness in detecting depression. So, this project can help such people to check on their own if they are depressed or not. Keywords: Depression Detection, Facial Expressions, Social media Analysis, Support vector machines (SVM), Natural language Processing (NLP), Speech Analysis, Machine Learning