Using Data Science To Calculate Accuracy of Online Depression Detection Questionnaire

Abstract

Depression is a common mental disorder. Globally, it is estimated that 5.0% of adults suffer from depression.Depression is a leading cause of disability worldwide and is a major contributor to the overall global burden of disease. Major depressive disorder (MDD), the clinical term for depression, is one of the most common mental health conditions, affecting an estimated 350 million people in all age groups. So there exist online tools for diagnosing depression. One such questionnaire for Depression is the Patient Health Questionnaire (PHQ-9). This Project will use data science to find out how accurate these questionnaires are based on questionnaire results, age, sex, medical illness history and diagnosed for depression by physician(Target) our goal is to find out if the questionnaire should be the first step in the roadmap to seek medical help. This project will be using various machine learning tools and functions to help find accuracy of online Depression detection systems such as KNN, SVM, DT and Logistic regression.The expected final outcome for this project will be a statistical analysis of output of depression detection questionnaires to that of clinical diagnosed data. Future scope of the project includes calculation of accuracy for systems available in future and be a tool for improvisation.