Data Mining Techniques for Drug Use Research

Data Mining Techniques 0
Data mining techniques for drug use research
[Name]
[University Affiliation]
[Date]

Data Mining Techniques 1
1- Introduction
The literature review consists of 7
articles that has been gathered by using
Scopus and Google Scholar database. The
keywords that has been used while searching
for the relevant literature are “Data mining
Techniques and Drug Use”.
It has been identified in the research
that random cluster sampling of school has
been taken into consideration from the
Island of Mallorca from which 22 schools
were taken into consideration [3]. The
researcher has worked on random cluster
sampling; this method is used because the
researcher is able to divide the population
into different groups namely cluster. In case
of a random sampling of cluster method, it
can be said that using random cluster
sampling; the researcher is able to classify
the data into different groups [3]. Under this
research, 9300 students aged between 14
and 18 has been taken to analyse the
variables, cluster sampling method has
helped the study to look for unreliable
answers of adolescents, the final sampling is
based on 9284 adolescents (among which
47.1% were boys, and 52.9% were girls with
an average age of 15.59 years) [1][3].
2- Critical Analysis
For this type of study, researchers
are supposed to seek permission from the
participants; they even want written consent
to avoid any legal or ethical issue towards
their research [3]. Therefore, the researcher
took voluntary and written informed consent
from the participants to follow the protocol;
a qualitative research approach has been
taken into consideration. Data mining
techniques are of different types, for
example, for this research Decision tree and
Artificial Neural Network has been adopted
to carry out statistical techniques to look for
the specific types of drugs these children
have used in their lives [3] [7]. Other data
mining technique include ANN data
processing system to structure the data and

Data Mining Techniques 2
developed functioning of biological
networks to outline the characteristics of
parallel processing with distributed memory
to adopt the surrounding using
backpropagation algorithm in analysing the
data [3].
The results determined that
approximately 52.7% students have drunk
alcohol, 25% of them have smoked tobacco,
18.6% students use cannabis, and 1.6%
students use cocaine [3]. The research has
used the predictive power of motives for
drug use, therefore, data mining predictive
model has been taken into consideration,
Decision Tree has been run to take logistic
regression, K-Nearest Neighbour, Naïve
Bayes, and Artificial Neural Network to
analyse drug substance and see if there is
any balance within it [1][6].
In another research titled
“Employing data mining to explore
association rules in drug addicts” the
researchers have determined that in normal
terms data mining are only taken as the
technique to extract knowledge implicitly
from the existing database. The research
follows a process to analyse and study a
huge amount of data in order to extract
underlying facts and figure to determine a
pattern of drug use, and its relationship with
legal jurisdiction to increase the importance
of data mining techniques in investigating
descriptive and predictive purposes. The aim
behind this is to unveil the facts and figures
to extract the data in an understandable
manner [7]. The researcher has to use the
exploratory technique to identify the
variables and characteristics of the database
in order to predict any future detail
associated with this research. It has been
determined by the researcher that there are
different data mining technique to employee
require a database and its results to work
with various kinds of discovered knowledge
[6][7]