부산대학교 도서관

Depression, also known as major depressive disorder, is a widespread psychological condition that has a negative influence on how somebody thinks, feels, as well as the way they behave. The good news is that it can also be treated. Depression is characterized by a lack of pleasure in formerly pleasurable activities as well as by emotions of despair. It often leads to diverse emotional and physical challenges and a reduction in the capacity to operate normally both at work and at home. As a result, there is a need for an automated system that is capable of assisting in the diagnosis of depression in individuals of all ages. Researchers have been exploring different ways to properly recognize depression in order to improve their ability to diagnose it. In this context, a variety of studies are being done. Techniques from the discipline of machine learning are currently being used to predict the possibility of psychological illnesses and subsequently produce potential treatment as well. The detection of depression was addressed in this research using various ML classifiers. A wide variety of ML methods, including SVM, Logistic Regression, CART, KNN, Random Forest, and Transfer Learning, were used in the process of depression detection. This paper provides a number of different AI and ML algorithms that can aid in the identification and analysis of emotions, and consequently depression along with the accuracy of these ML techniques.