Techniques of Predictive Analysis

Techniques of predictive analysis
Techniques of Predictive Analysis
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Abstract-The world of business today is
filled with magnitudes of data, the
quantity of data being collected by
various industries is continuously
flourishing as digital technology continue
to penetrate the industries. The increased
exploitation of a number of novel
inventions and the impact of social media
has availed a large data set termed as big
data. This data has the capacity to
generate very effective information if
appropriate models are applied to analyse
it. Organization that were previously in
the verge of collapse can instantly rise to
success curtesy of the big data technology,
for this reason several industries are
increasingly paying attention to the
various predictive models that can be
applied to assist forecast future states of
nature. The predictive analysis composed
of a number of statistical and analytical
techniques that can be applied to develop
models for predicting future possibilities.
For this reason, predictive analysis has
proved to be a vital area in cases where
substantial quantity of sensitive data
needs to be analysed. With the aid of the
data mining techniques, future
probabilities and measures can be
forecasted and the predictions used to
improve strategic decision making. This
literature review identifies clear ideas of
the applications of the data mining
techniques and the sue of the predictive
analytics on different areas such as
medical field and business in particular.
I. Literature review
A. Introduction
Predictive analytics is the science
and art of developing predictive models
which can afterwards be applied to predict
selected outcomes in the future with a higher
probability of occurrence than would be
under a normal gauze. Predictive analysis is
often applied as an umbrella term that also
entails other forms of advanced analytics
such as the descriptive analytics that focuses
on giving an insight of past occurrences. The
concept of predictive analytics applies large
and varying techniques to assist firms
foresee the future. As the idea of big data
continues to take over business strategic
decision making, the application of
predictive analysis is continuously becoming
popular in firms [1]. Technologies like
machine learning, txt analysis as well as
neural networking are some of the
applications of predictive analysis in
business.
The application of big data has gained
popularity in most of the organisations
operating globally, through the use of big
data firms are relying on the available
information to evaluate expected consumer
reactions, gauge potential products demand
as well as analysis of the financial impact of
management decisions. The predictive
analysis techniques apply technology to

Techniques of predictive analysis
forecast the future occurrence and enable the
managers to act accordingly. The ability to
foresee the future is a very significance
concept in a business. Through accurate
predictions firms are able to take steps that
will improve their future competitive
advantage. Global business is increasing
becoming competitive, the techniques of
predictive analysis are thus a significance
avenue for meandering the competitive
business world. The ability to predict the
future do eventually define the firms that
comes out on top in the long run.
B. Summary of articles
The concept of predictive analytics has
been in the past applied in the area of data
mining to forecast future events especially in
the field of medicine, education as well as
crime detection. The health sector does
contain bulky information that if used
effectively can be significant in making
crucial decisions. The research conducted by
Babu and Sastry [1] was focused on the
enterprise resource planning (ERP)
predictive features. The study objective was
on how the current and past data can be
analysed and applied to identify probable
risks that organisations might be facing.
Analytical decision tools are utilised by the
system to make decisions as a way of
improving service delivery. A study by
Bellaachia and Guven [2] did suggest that
data mining can be efficiently applied to
forecast lastingness of breast cancer. The
authors did examine three methods applied
in data mining (neural networks, Bayes and
Naïve) to show that data mining is the apt
technology that can identify patterns in a
data set derived from the health sector. Even
though some diseases are more difficult to
predict due to their complexity the
application of predictive together with
advanced skill in the medical field can assist
identify some trends that may assist lead to
breakthrough. To study heart diseases by
applying the classification algorithms, data
mining algorithms like naïve Bayes can be
useful in forecasting heart attacks. From the
author’s analysis the use of this tools can
give up to a 99% accurate prediction [3].
The data mining techniques did prove that
diagnoses can be predicted when the right
techniques of predictive analysis are applied
in the right manner.
A survey conducted regarding
predictive analysis by using big data did
show that large volumes of medical data can
only be processed accurately by applying
very powerful analytical tools. Techniques
under data mining can also be applied in the
analysis of trend in diseases. Ramaraj and
Thanamani [4] proposed that heart diseases
can be significant identified by the use of
predictive analytics. The objective of the
researchers was to design a predictive
method that can be applied in the detection
of heart diseases. From their conclusion, the
use of CN2 rule is best fitted for doing
classifications compared to any other
technique.
A study by Nasridinov et al [5] did
analyse several data mining with generated
test data meant to evaluate the most efficient
technique to predict patterns in crimes. The
researches did focus mostly on the extensive
performance analysis of several data mining
algorithms. The study was based on the
assumption that wearable sensors are
attached to the clothes of the users of the
identified techniques. The sensor would
capture the user’s inner temperature and
heartbeat and afterwards send the
information to the servers to perform
emotion miming. Danger was indicated wen