Accuracy Analysis on Online Depression Detection Using Machine Learning and Data Science

Depression is a psychological well-being disorder. As per W.H.O It has been assessed that generally 5.0% of grown-ups experience the ill effects of depression. Depression is likewise known to be one of the main sources of handicap around the world. The death toll has been increasing year after year. It has been found that various treatments for mild, moderate and severe depression exists, but it's hard to detect and determine the severity of depression accurately. Thus, Author will use diagnostic data from PHQ- 9(Patient health questionnaire) and determine accuracy of the output. Since PHQ-9 is the standardized questionnaire used by doctors and hospitals as the first step in determining the severity of depression in a patient. Author has using 4 Machine learning models: KNN, Decision Tree, Logistic regression and SVM and then cross validating those results to determine the accuracy of PHQ-9 results. These Learning models are chosen as currently they are the best type of regression models for our use case, a similarity which can be observed in heart disease classification, which has been a big reference for this paper.