Open AccessDepression Analysis using Machine Learning Based on Musical Habits
Author(s) -
Suyoga Srinivas,
Naveen N. Bhat,
Yashwanth Chandolu,
M R Naveen,
Mr Yashwanth,
Venkat Chandolu
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.b1016.1292s19
Subject(s) - depression (economics) , psychology , popularity , regression analysis , regression , musical , variable (mathematics) , clinical psychology , cognitive psychology , computer science , machine learning , social psychology , mathematics , psychotherapist , art , mathematical analysis , economics , visual arts , macroeconomics
Depression has been a main cause of mental illness. Depression results in vital impairment in lifestyle. A significant reason for suicidal cerebration is observed to be depression. Music varies the intensity of emotional experience by captivating the neurotransmitters and brain anatomy, including the brain’s dopaminergic projections. The popularity of using Regression Models in data analysis in both research and industry has driven the development of an array of prediction models. It relies on independent variables and can provide the prediction for the dependent variable. The paper outlines the development of a Regression model to get the depression score of a person based on the music the user listens to. A regression model is used to predict the depression score depending upon the data obtained from a varied span of individuals and the genre of music they have listened to. We generate a suitable report based on the depression score. The doctor can then use the report to give the necessary treatment to the depressed patient. With our research, we have obtained variance and r2 score of over 0.95.