phat 20(21): e5

Research Article

Consumption of Licit and Illicit Substances leading to Mental Illness: A Prevalence Study

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  • @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
Bijoy Chhetri1, Lalit Mohan Goyal1, Mamta Mittal2,*, Sandeep Gurung3
  • 1: Department of Computer Engineering, J C Bose University of Science and Technology, YMCA, Faridabad, India
  • 2: Department of Computer Science and Engineering, G B Pant Government Engineering College, Okhla, New Delhi, India
  • 3: Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim, India
*Contact email: mittalmamta79@gmail.com

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.