Data Mining Techniques in Drug Use: A Comprehensive Analysis

This paper provides a significant knowledge regarding data mining technique in drug use. It discusses various research methods, findings, and limitations. The objective is to investigate the causes why students utilize drugs using data mining techniques. The paper highlights the importance of data mining in drug discovery and prevention. The sources used include Google Scholar, IEEE explorers, ACM digital library, and SAGE journals. The research methods include interviews, online surveys, and questionnaires. The findings show the effectiveness of data mining algorithms in analyzing drug use patterns. However, the research has limitations in terms of sample size and reliability of data.

Research 1
Applied Research Methods

Research 2
Contents
Introduction......................................................................................................................................3
Sources.............................................................................................................................................3
Discussion of methods.....................................................................................................................4
Findings...........................................................................................................................................6
Limitations.......................................................................................................................................6
Conclusion.......................................................................................................................................7
References........................................................................................................................................8

Research 3
Abstract
This paper provides a significant knowledge
regarding data mining technique. The whole
description regarding data mining techniques is
provided by the author in an effective manner.
Various papers of different authors are
highlighted in this paper in order to bring
comprehensive understanding regarding the data
mining techniques.
Keywords: Data mining technique, Machine
Learning, techniques, sampling method
Introduction
The techniques of data mining show different
benefits that can help to develop understanding
regarding prolonged drug use. Data mining is the way
of arranging things by huge data sets in order to
recognize patterns and create relationships so that
issues can be resolved by data analysis. Data mining
is referred as an examination on expansive
arrangements of information. It utilizes devices like
association, grouping, division and arrangement for
better control on the data to help the different firms
for competing on lower costs, while improving the
nature of drug discovery and delivery methods [1].
For data mining study, the researcher needs to take
permission from the participants and they even would
require written consent in order to avoid issues
related to legal and ethical towards the research. The
data mining techniques are the completely different
approach for researching particular thing and that is
why the researcher needs voluntary and written
informed consent from the persons to take the
protocol, for which the researcher has to go with the
qualitative method. For data mining techniques, it is
quite necessary to keep the focus on various aspects
such as Decision tree and Artificial Neural Network
in order to handle statistical techniques to look for the
various kinds of drugs, which are being used by
youth generation in their lives. There is another kind
of data mining technique of ANN data processing
system to framework the data and improve the
functioning of biological networks to structure the
features of parallel processing with characterized
memory to take on the surroundings[6].
The main objective of this report is to investigate, by
data mining techniques, the causes why students
utilize drugs. This paper will review the method for
critically analyze the data mining techniques for
which number of methods taken into consideration in
different research papers. This activity will lead the
researcher in a great way of analyzing the ongoing
situation of drug use.
Sources
Data mining is the way of getting information from
huge sets of selected data by the utilization of
algorithms and techniques drawn from the area of
Machine Learning, Statistics and Data Base
Management Systems. An assortment of choice will
be important to build the efficiency of medical
personnel, break down consideration results, and
ceaselessly refine care conveyance procedures to stay
beneficial while holding the line on expenses and
maintaining the quality of care. Investigating the
research paper with the concerned research questions
is quite a difficult task and need outstanding
techniques. The use of Google Scholar, IEEE
explorers, ACM digital library and SAGE journals
are considered to find out various views of the
researcher on a selected topic. To get enough
information about the selected topic, the use of
different keywords are taken into consideration for
the research paper in which these are some of them:
use of data mining technique in drug use, the
importance of data mining in the research and drug
discovery.
Discussion of methods
There are number of research methods to conduct the
research and most of them fail in the group of the
qualitative as well as quantitative approach. There are
number of papers related to the selected topic in
which various authors have used different approach
and methods to analyze the data. Some of them have
taken consideration of interviews in the form of
research methods and some of them have used the
online surveys and questionnaires. The discussion
will be made regarding various methods in which
they used in number of papers and will be analyzing
them critically.
Applied Business Research

Research 4
A study conducted byJimenz, Anupol, Cajal and
Geervilla depicted the factors which were influencing
the data mining acceptance in the market by
reviewing the various techniques of data mining. A
random cluster sampling techniques are being used in
the paper in which the quantitative approach has been
used by the researcher [2].The researcher has used
cluster sampling technique for getting reliable
information. The young people secretly addressed a
questionnaire, which got some information about the
recurrence of the utilization of deferent addictive sub-
positions just as the intentions of utilizing drugs. The
researcher dissected medication use thought
processes through a progression of techniques
incorporated into Data Mining: two traditional
machine learning techniques, Decision Trees (DT)
and Artificial Neural Networks (ANN);two current
factual methods, k-Nearest Neighbors (K-NN)
andNaïve Bayes (NB); and an established measurable
system, LogisticRegression (LogR). Various tables
regarding motives adolescents provide to use
addictive substances by the substance they use. The
researcher has used alcohol, tobacco, cannabis and
cocaine. The research used the data mining method to
compare classical and modern data mining
techniques which are taken into consideration barely
in the drug related to exploring the reasons differ
from the kind of substance used. The researcher has
concluded that never drug users perceive that youth
use drugs as friend consume, to forget issues. On the
other hand, with the help of the quantitative research
method, it has come to know that many adolescents
consume alcohol and data mining techniques are
useful to evaluate substances risk and preventive
aspects [2].
Although more focused and systematic study as
compared to above is conducted by Pal and
Chaurasia, researchers argue that a number of
researchers have focused on the previous research
that has main focus on the usability and utility
aspects of the system and fewer studies have taken
the trustworthy opinion of the people regarding the
data mining research. This paper elaborates four
popular data mining algorithms such as Sequential
minimal optimization (SMO), Bagging, REP Tree
and decision table (DT) detached from a decision tree
or rule-based classifier to get better the competence
of academic performance in the instructive
establishment for students who use alcohol.
This examination tries to set up the connection
between's poor scholastic execution and the
utilization of alcohols in school grounds. The
exploration will likewise evaluate the different
reasons regarding why understudies misuse
medications and alcohols. Behavior is a noteworthy
part of life, after getting an observation of the
students by the researcher in this paper; this
exploration has prescribed methods for restoring
those officially influenced and methods for
eradicating peddling business going on at our
Universities [3].
To conduct this research, the researcher has used data
set by MCA Department on the sampling method, in
which the sample size they preferred of 200 students.
In this step, the researcher has used data in a separate
level and joins them in a single table. The researcher
has mainly focused on the data selection and
transformation and involves only those fields that
require data mining technique. The first table is
related to the student related variables and the second
table is related to the performance of the classifiers.
In the conclusion, the researcher has concluded that
the best calculation dependent on the understudy
alcohol data is Bagging Classification with the
precision of 80.2532 % and the all-out time is taken
to fabricate the model is at 0.14 seconds. These
outcomes propose that among machine learning
algorithm tested, Bagging classifier can possibly
altogether improve the regular characterization
techniques utilized in the examination [3].
On the other hand, the different and vague approach
has been considered by Sakaray, Kankariya,
LullaAgarwal and Alappanavar, in comparison of
the above researchers. The investigation that has been
done in different organizations is as yet inadequate to
distinguish the accurate measures that are required
for the foundations to yield better outcomes.
Numerous data sets gathered from various sources,
which are profoundly vulnerable to absent and
conflicting data because of which there will be low-
quality mining results. The researcher utilizes
extraordinary methods for information pre-preparing
and data cleaning and merges two diverse datasets by
utilizing information coordination. There are diverse
purposes behind off base information as there might
be human or computer mistakes happening while data
entry [4]. According to them, their surveys affirm
that execution of neural system when contrasted with
different calculations referenced in the paper like
choice trees, Multi Linear regression and Apriori
yields much better outcomes as far as precision,
consistency and versatility. The review also
demonstrates that the execution factor of neural
system increments with the expansion in dataset
estimate as this calculation takes persistent qualities.
The conventional method has been taken into
consideration for evaluating the results. The work of
them can be tried by utilizing constrained dataset as

Research 5
the complexity of classifier is higher than that of
other calculations. As per them, machine learning can
contribute a ground-breaking prescient device which
can support the different establishments and just as
understudies to assess themselves at the starting
dimension of their scholarly stage[4].
On the other hand, the different approach has been
used by the researcher in order to attain the objectives
of the selected topic. In this research, the researcher
has mainly focused on the huge data sources, in
which the data mining has been considered by them
to investigate knowledge implicit in them; the results
of the researcher can be taken into consideration as
knowledge-based support system for the purpose of
making good decisions in the context of the addiction
prevention as well as treatment. For their research,
the researcher has involved 471 participants in which
the proportion has been divided into male and
females, where 86.2% were male and 13.8% were
female. The aim of this study is to remove rules from
the gathered data by taking consideration of the
associations’ model. The researcher has used
quantitative approach and an online survey to collect
the information because with the help of this
approach, the research can get reliable information.
The aim of the study is to remove rules from the
gathered data by utilizing association models. The
results can be utilized by rehab clinics to provide
knowledge about bonding between various
parameters and facilitate them for effective
treatments. The finding demonstrates that there is a
noteworthy bonding between individual features and
LSD abuse, the committing crimes and family history
of drug addiction[5].
Findings
There are number of researchers had endeavored to
define the various methods of data mining in drug
use. Every study has taken consideration of different
methods involving online surveys, interviews,
secondary surveys and literature survey. It has been
found that some of them were effective in the relation
of the approach, on the other hand, some of them
were limited in the relation of audience and some of
them were related to the calculated guesses in which
truth can be hard to find out.Sakaray, et. al., used
literature survey for the purpose of getting enough
information regarding the selected topic. The main
focus of the researcher was on tweet based on the
classifications developed in the content analysis
stage. Structured and unstructured data had been used
by the researcher.
However,Zahedi and Zare-Mirakabad had studied on
471 participants by using associations’ models so that
the results can be used by the rehab centre for
knowing the relationship between different
parameters[5]. On the other hand, Pal and
chaurasiaused data set by MCA Department on the
sampling method in which the sample size they
preferred of 200 students [3]. Four popular data
mining approach has been considered by the
researcher that is data mining algorithms, Sequential
minimal optimization (SMO), Bagging, REP Tree
and decision table (DT). The use of methods has been
taken into consideration by the researcher to improve
the efficiency of the students who consumed
alcohol[4].
Various specific tables have been used by the
researcher. Jimenez, Anupol, Cajal and Gervilla have
used random cluster sampling method in which five
classifiers techniques are used. The researcher
highlighted the alcohol use classification pruned tree
which is one of the data mining techniques [2]. This
paper has involved the classical and modern data
mining technique which is used in the drug
concerned context for the purpose of examining the
results. Exploratory point of view has been used by
the researcher to examine the outcomes.
Limitations
Every research has its own strengths and drawbacks,
which are relied on the kind of question researchers
have explored. In this case, a number of researchers
had used a number of research methods to explore
various data mining techniques of drug user.
However, research has used sampling method but
there are some limitations which bound them to move
beyond the selected topic. This method can be
effective for many types of research, but for this
research, it has some limitations such as lack of
sample size and less reliable data as many people
have not enough knowledge regarding the same and
provide details as per assumption. It shows doubt in
the quality of the work.
Many researchers have used an interview research
method to get information about the selected data.
The interview is an effective method for data
collection that captures emotions, non-verbal
behaviour, and verbal behavior. On the other hand,
this study has used only a limited number of
participants that was tough to describe the originality.
However ,Zahedi and Zare-Mirakabad, this research
had studied on 471 participants by using associations’
models so that the results can be used by the rehab

Research 6
centre for knowing relationship between different
parameters. Along with that, for sampling they
involve 200 participants in it which is tough to
evaluate in a one go. It is time consuming and due to
lack of time, the results can come in less productive
manner. Sakaray, et. al., used literature survey which
provide good results regarding the chosen topic, but it
has some limitations as well because it is not required
that every research has same point of view, it makes
confusion in the research.
Conclusion
It can be concluded from method review that the
point of view of every research towards drug use is
entirely different from each other. It has been found
that some researchers use qualitative method and
some of them use quantitative method to get data.
Data mining techniques are being used by many
researchers in different form to analyze the scope of
drug use. Interviews, surveys, literature survey and
questionnaires were taken into consideration by the
researcher to get relevant data. The discussion has
been made in the context of various methods and
found that methods rely on the nature of the research.
Various articles have been reviewed by the researcher
in an effective manner and it has been found that
every research put their best to reach the conclusion.
The researchers had used different sample size to get
enough information regarding the same and there are
some limitations as well that prevent them to go
beyond the research topic. If researchers had used
more participants from a number of backgrounds, it
would become more generalized research. On the
other hand, interviews have been taken by the
researcher on the serious way in which they prepare a
questionnaire to ask the question, and the same had
been used by them to get an answer from participants
face to face.

Research 7
References
N. Yassein, R. M Helali and S. Mohomad,
"Predicting Student Academic Performance in KSA
using Data Mining Techniques", Journal of
Information Technology & Software Engineering,
vol. 07, no. 05, 2017. Available: 10.4172/2165-
7866.1000213.
R, Jimenz, J, Anupol, B. Cajal and E, Geervilla.,
“Data mining techniques for drug use
research,”Addictivebehaviour reports pp. 128-135.
S. Pal, and V. Chaurasia, “Is Alcohol Affect Higher
Education Students Performance: Searching and
Predicting Pattern Using Data Mining Algorithms”,
Interenational journal of advance research in science
and enginnering. Vol. No. 6 Issue 2 pp 238-248.
P. Sakaray, S. Kankariya, C. Lulla, Y. Agarwaland ,.
Alappanavar, "Review on Student Academic
Performance Prediction using Data Mining
Techniques", International Journal of Advanced
Research in Computer and Communication
Engineering, vol. 6, no. 2, pp. 301-302, 2017.
Available: DOI 10.17148/IJARCCE.2017.6270.
[5] F. Zahediand M. R. Zare-Mirakabad"Employing
data mining to explore association rules in drug
addicts", Journal of AI and Data Mining, vol. 2, no.
2, pp. 135-139, 2014.
[6]A. Sarker and G. Graciela, "Data, tools and
resources for mining social media drug
chatter", Proceedings of the Fifth Workshop on
Building and Evaluating Resources for Biomedical
Text Mining, pp. 99-147, 2019.