Detecting Depression Using Quality-of-Life Attributes with Machine Learning Techniques

Worldwide, depression affects millions of individuals even without their knowledge and is a crippling affliction. Primary care physicians frequently discover that they must treat mental health problems like depression despite having little or no formal training in how to do so. There is proof that an integrated strategy, where doctors regularly screen patients for mental health issues and collaborate with psychologists and other mental health specialists to treat patients, results in lower costs and improved patient outcomes. In order to handle and study the heterogeneous data and understand the correlation between aspects of quality of life and depression, this paper uses machine learning techniques. Machine learning is used to predict people who might have depression based on data that is found in CDC National Health and Examination Survey (NHAES) website. These forecasts could be used to more quickly and easily connect patients with qualified mental health specialists.