Exploration of Risk Factors Associated with Adolescent Drug Use through Cutting Edge Recursive Partitioning Techniques

Vijay Kumar Tiwari, Kuldeep Kumar, Sherin Raj

Research output: Contribution to journalArticleResearchpeer-review

7 Downloads (Pure)

Abstract

The purpose of this article is to explore and identify risk factors influencing drug use in school going adolescents aged 10 to 19 in a hilly state in the North-Eastern part of India. This article will explore the data collected from the National Institute of Health and Family Welfare, New Delhi, by using cutting edge Recursive Partitioning techniques such as Discriminant Analysis, Decision Tree Method, Artificial Neural Network etc to build a predictive model. Out of 3069 randomly selected participants who undertook the Adolescent Reproductive and Sexual health (ARSH) questionnaire a subset have been used to form this data set. Statistical techniques like Independent T-Tests, Chi Square test for independence, Logistic Regression, Discriminant Analysis, Artificial Neural Networks (ANN) were used for the exploration of data. These techniques were found to be extremely useful in the prediction of associated risk factors that contribute to consumption of banned drugs among adolescents. The recursive techniques addressed in this article are becoming useful predictive instruments not only in the context of drug misuse; however, for other socio-health problems such as alcohol consumption, adolescent sex behaviour and burden of disease.
Original languageEnglish
Pages (from-to)81-94
Number of pages14
JournalDemography India: population - society - economy - environment - interactions
Volume46
Issue number2
Publication statusPublished - 2017

Fingerprint

drug use
adolescent
discriminant analysis
neural network
health
drug
family welfare
sex behavior
predictive model
alcohol consumption
logistics
India
Disease
regression
questionnaire
school

Cite this

@article{ab14e460bba743799b54f56a0dcc4c03,
title = "Exploration of Risk Factors Associated with Adolescent Drug Use through Cutting Edge Recursive Partitioning Techniques",
abstract = "The purpose of this article is to explore and identify risk factors influencing drug use in school going adolescents aged 10 to 19 in a hilly state in the North-Eastern part of India. This article will explore the data collected from the National Institute of Health and Family Welfare, New Delhi, by using cutting edge Recursive Partitioning techniques such as Discriminant Analysis, Decision Tree Method, Artificial Neural Network etc to build a predictive model. Out of 3069 randomly selected participants who undertook the Adolescent Reproductive and Sexual health (ARSH) questionnaire a subset have been used to form this data set. Statistical techniques like Independent T-Tests, Chi Square test for independence, Logistic Regression, Discriminant Analysis, Artificial Neural Networks (ANN) were used for the exploration of data. These techniques were found to be extremely useful in the prediction of associated risk factors that contribute to consumption of banned drugs among adolescents. The recursive techniques addressed in this article are becoming useful predictive instruments not only in the context of drug misuse; however, for other socio-health problems such as alcohol consumption, adolescent sex behaviour and burden of disease.",
author = "Tiwari, {Vijay Kumar} and Kuldeep Kumar and Sherin Raj",
year = "2017",
language = "English",
volume = "46",
pages = "81--94",
journal = "Demography India: population - society - economy - environment - interactions",
issn = "0970-454X",
number = "2",

}

Exploration of Risk Factors Associated with Adolescent Drug Use through Cutting Edge Recursive Partitioning Techniques. / Tiwari, Vijay Kumar; Kumar, Kuldeep; Raj, Sherin.

In: Demography India: population - society - economy - environment - interactions, Vol. 46, No. 2, 2017, p. 81-94.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Exploration of Risk Factors Associated with Adolescent Drug Use through Cutting Edge Recursive Partitioning Techniques

AU - Tiwari, Vijay Kumar

AU - Kumar, Kuldeep

AU - Raj, Sherin

PY - 2017

Y1 - 2017

N2 - The purpose of this article is to explore and identify risk factors influencing drug use in school going adolescents aged 10 to 19 in a hilly state in the North-Eastern part of India. This article will explore the data collected from the National Institute of Health and Family Welfare, New Delhi, by using cutting edge Recursive Partitioning techniques such as Discriminant Analysis, Decision Tree Method, Artificial Neural Network etc to build a predictive model. Out of 3069 randomly selected participants who undertook the Adolescent Reproductive and Sexual health (ARSH) questionnaire a subset have been used to form this data set. Statistical techniques like Independent T-Tests, Chi Square test for independence, Logistic Regression, Discriminant Analysis, Artificial Neural Networks (ANN) were used for the exploration of data. These techniques were found to be extremely useful in the prediction of associated risk factors that contribute to consumption of banned drugs among adolescents. The recursive techniques addressed in this article are becoming useful predictive instruments not only in the context of drug misuse; however, for other socio-health problems such as alcohol consumption, adolescent sex behaviour and burden of disease.

AB - The purpose of this article is to explore and identify risk factors influencing drug use in school going adolescents aged 10 to 19 in a hilly state in the North-Eastern part of India. This article will explore the data collected from the National Institute of Health and Family Welfare, New Delhi, by using cutting edge Recursive Partitioning techniques such as Discriminant Analysis, Decision Tree Method, Artificial Neural Network etc to build a predictive model. Out of 3069 randomly selected participants who undertook the Adolescent Reproductive and Sexual health (ARSH) questionnaire a subset have been used to form this data set. Statistical techniques like Independent T-Tests, Chi Square test for independence, Logistic Regression, Discriminant Analysis, Artificial Neural Networks (ANN) were used for the exploration of data. These techniques were found to be extremely useful in the prediction of associated risk factors that contribute to consumption of banned drugs among adolescents. The recursive techniques addressed in this article are becoming useful predictive instruments not only in the context of drug misuse; however, for other socio-health problems such as alcohol consumption, adolescent sex behaviour and burden of disease.

UR - http://demographyindia.in/

M3 - Article

VL - 46

SP - 81

EP - 94

JO - Demography India: population - society - economy - environment - interactions

JF - Demography India: population - society - economy - environment - interactions

SN - 0970-454X

IS - 2

ER -