Research Article
Consumption of Licit and Illicit Substances leading to Mental Illness: A Prevalence Study
@ARTICLE{10.4108/eai.11-5-2020.164415, author={Bijoy Chhetri and Lalit Mohan Goyal and Mamta Mittal and Sandeep Gurung}, title={Consumption of Licit and Illicit Substances leading to Mental Illness: A Prevalence Study}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={6}, number={21}, publisher={EAI}, journal_a={PHAT}, year={2020}, month={1}, keywords={Mental Illness, Depression, Anxiety, Machine Learning, Support Vector Machine, Feature Selection, Cross Sectional Study}, doi={10.4108/eai.11-5-2020.164415} }- Bijoy Chhetri
Lalit Mohan Goyal
Mamta Mittal
Sandeep Gurung
Year: 2020
Consumption of Licit and Illicit Substances leading to Mental Illness: A Prevalence Study
PHAT
EAI
DOI: 10.4108/eai.11-5-2020.164415
Abstract
Background: A menace case of drug & narcotics abuse has been in prime focus of the society nowadays. Therefore, the need of technological intervention is primary concern to examine the prevalence, severity and outcome to the drug menace and its consequences.
Objective: This study is to suffice clinical decisions through behaviour observatory data through preliminary screening of prevalence, correlation and severity of illness.
Method: The model has been proposed to check for General Anxiety Disorder and Depression of a subject abusing any of the drug/marijuana/alcohol. In this model data set of Sikkim’s youth has been considered to find relation of addiction leading to mental disorder.
Result: This proposed system has been successful to associate any form of substance abuse to to some of illness to a limit of .83 accuracy scored by Support Vector Machine over the other machine learning models. The model has been deployed and being observed in few of the rehabilitation centre.
Copyright © 2020 Bijoy Chhetri et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
