Open AccessDepression Predictor Model for Farmers using Machine Learning Techniques
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b1047.1292s19
Subject(s) - depression (economics) , categorization , psychology , mental state , electroencephalography , subject (documents) , cognitive psychology , artificial intelligence , psychiatry , computer science , library science , economics , macroeconomics
There are a few disorders that are the outcome of unbalanced mental state. A very basic one is depression. Depression is a very serious yet common mental ailment that damagingly distresses how a person thinks or feels or acts. Side effects of physical injuries are obvious and regularly agonizing, because of which they are recognized and paid attention to. Symptoms of mental illnesses are not very comprehendible. A lot of individuals don't know about them, including the people who are suffering. This research paper proposes a methodology with an approach to machine learning in order to categorize the subject into 4 distinguished levels of depression, namely normal, mildly depressed, moderately depressed and severely depressed. This procedure is proposed to be carried out using PHQ-9 and DASS-21 questionnaire and the electric EEG bands Alpha, Beta, Delta, Gamma and Theta variations will be obtained via the usage of head kit Neurosky’s Mindwave aid.