Open AccessTo Analysis the Relation Between Internet Usage and Depression with Machine Learning
Author(s) -
Amarjit Malhotra,
Megha Gupta,
Vineeta Gupta,
Sanchit Shokeen,
Ankit Singla
Publication year - 2022
Publication title -
international journal of advanced trends in computer science and engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3091
DOI - 10.30534/ijatcse/2022/041112022
Subject(s) - depression (economics) , the internet , internet privacy , computer science , relation (database) , social media , mental health , machine learning , supervised learning , artificial intelligence , population , world wide web , psychology , psychiatry , data mining , medicine , environmental health , artificial neural network , economics , macroeconomics
Depression is a major disorder in the population of the 21st century. Previous studies have associated depression with internet usage and as the access to internet spreads through the developing countries depression can prove to be a challenging issue to combat. This paper proposes a new method to detect the presence of general depression among social media users using features extracted from their online habits including browsing, streaming, games and social media. The pertinent data has been collected from individuals primarily based in developing countries and applied various established supervised machine learning algorithms to predict the presence of general depression. Preliminary testing shows that the proposed system performs rather well and enables an easy method for keeping online mental health in check without compromising on privacy. The proposed work shows a clear positive correlation between social media usage and general depression highlighting the inimical effects of elevated usage.