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Effect of Data Size on Feature Set Using
Classification in Health Domain
Uttham H1*
, Gowramma2
1
PG-Student, 2
Associate Professor,
Dept. Computer Science & Engineering, D.B.I.T, Banglore, Karnataka, India.
1*
utthamhmanju@gmail.com,2
gowramma@gmail.com.
ABSTRACT:
In health domain, the major critical issue is prediction of disease in early stage. Prediction
of disease is mainly based on the experience of physician so many machine learning
approach contribute their work in the prediction of disease. In existing approaches, either
prediction or feature selection has been concentrated. The aim of this paper is to present
the effect of data size and set of features in the prediction of disease in health domain using
Naïve Bayes. This shows how each attribute or combination of attribute behaves on
different size of dataset.
Keywords: Machine Learning, Classification, Naïve Bayes, feature selection.
1. INTRODUCTION
In health, domain diagnosis of disease is
very challenging task. Earlier prediction can
made based on some lab test. Using this lab
test report the physician will decide whether
the patient has disease or not but prediction
of disease by physician mainly depend on
the experience. If the physician has more
experience, then he may predict well. if the
physician has less experience then he may
predict wrongly.to overcome from this
problem machine learning has many
approaches like KNN, SVM, ANN to

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predict correctly. Machine learning is a
branch of science that allows machine to
make decision.to make decision machine has
to learn on itself or by experience. There are
three types of learning supervised learning,
unsupervised learning, reinforcement
learning.
The aim of this study is to find the effect on
performance of different feature set using
WEKA on different size of Pima Indian
Diabetes Dataset. A critical challenge in
medical science is to attain the diagnosis
correctly. For correct diagnosis, generally
many tests done to predict correctly. All of
these test procedures said to be necessary in
order to reach the ultimate diagnosis.
However, on the other hand, too many tests
could complicate the main diagnosis process
and lead to the difficulty in obtaining the
results, particularly in the case where many
tests performed. This kind of difficulty
could be resolved with the aid of machine
learning which used directly to obtain the
result with the aid of several classification
techniques. Machine learning covers such a
broad range of processes that it is difficult to
define it precisely. A dictionary definition
includes phrases such as to gain knowledge,
understanding of, or skill by studying the
instruction or experience and modification
of a behavioural tendency by experienced
zoologists and psychologists study learning
in animals and humans [1]. The extraction of
important information from a large pile of
data and its correlations is often the
advantage of using machine learning.
Humans are constantly discovering new
knowledge about tasks. There is a constant
stream of new events in the world and
continuing redesign of Artificial Intelligent
systems to conform to new knowledge is
impractical but machine-learning methods
might be able to track much of it [1]. There
is a substantial amount of research has been
done with machine learning algorithms such
as Bayes network, Multilayer Perceptron,
Decision tree and pruning like J48graft,
C4.5, Single Conjunctive Rule Learner like
FLR, JRip and Fuzzy Inference System and
Adaptive Neuro-Fuzzy Inference System.
2. RELATED WORK
A good number of researches have been
reported in literature on diagnosis of
different deceases. Sapna and Tamilarasi [2]

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proposed a technique based on neuropathy
diabetics. Nerve disorder is caused by
diabetic mellitus. Long term diabetic
patients could have diabetic neuropathies
very easily. There is fifty (50%) percent
probability to have such diseases which
affect many nerves system of the body. For
example, body wall, limbs (which called as
somatic nerves) could be affected. On the
other hand, internal organ like heart,
stomach, etc., are known as automatic
nerves. In this paper, the risk factors and
symptoms of diabetic neuropathy are used to
make the fuzzy relation equation. Fuzzy
relation equation is linked with the
perception of composition of binary
relations that means they used Multilayer
Perceptron NN using Fuzzy Inference
System.
Leonarda and Antonio [6] proposed
automatic detection of diabetic symptoms in
retinal images by using a multilevel
perceptron neural network. The network
trained using algorithms for evaluating the
optimal global threshold, which can
minimize pixel classification errors. System
performances evaluated by means of an
adequate index to provide percentage
measure in the detection of eye suspect
regions based on neuro-fuzzy subsystem.
Radha and Rajagopalan [4] introduced an
application of fuzzy logic to diagnosis of
diabetes. It describes the fuzzy sets and
linguistic variables that contribute to the
diagnosis of disease particularly diabetes. As
we all know fuzzy logic is a computational
paradigm, that provides a tool based on
mathematics which deals with un- certainty.
At the same time this paper also presents a
computer-based Fuzzy Logic with maximum
and mini- mum relationship, membership
values consisting of the components,
specifying fuzzy set frame work. Forty
patients’ data have been collected to make
this relationship more strong.
Faezeh,Hossien, Ebrahim [7] proposed a
fuzzy clus- tering technique (FACT) which
determined the number of appropriate
clusters based on the pattern essence. Dif-
ferent experiments for algorithm evaluation
were per- formed which showed a better
performance compared to the typical widely
used K-means clustering algorithm. Data

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was taken from the UCI Machine Learning
Repository [3].
3. DATA SET DESCRIPTION
The characteristics of the data set used in
this research are summarized in following
Table 1. The detailed descriptions of the
data set are available at UCI repository
which contains 768 instances [3]
Dataset->Pima Indian diabetes
No of example->768
Input attribute->8
Output classes->two
Total number of attribute->nine
Missing attributes status->No
Noisy attribute status->No
Table 1. Characteristics of data sets
Sl number Attributes
0 Number of times
pregnant
1 Plasma glucose
concentration a 2
hours in an oral
glucose tolerance
test
2 Diastolic blood
pressure (mm Hg)
3 Triceps skin fold
thickness (mm)
4 2-hour serum insulin
(mu U/ml)
5 Body mass index
(weight in kg/(height
in m)^2)
6 Diabetes pedigree
function
7 Age (years)
8 Class variable (0 or
1)
4. METHODOLOGY
In this paper, we will use machine learning
techniques like the Naïve Bayes
classification techniques for classification of
diabetes data

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4.1. Naïve BayesThe Naïve Bayes [5]
classifier provides a simple approach, with
clear semantics, representing and learning
probabilistic knowledge. It is termed naïve
because is relies on two important
simplifying assumes that the predictive
attributes are conditionally independent
given the class, and it assumes that no
hidden or latent attributes influence the
prediction process.
Naive Bayes: The Naive Bayes classifier is
a simple supervised learning probabilistic
classifier based on Bayes’ theorem.
P(c|x) =P(x|c)P(c)/ P(x)--------->(1)
P(c|x) = P(x1|c)P(x2|c)...P(x6|c)P(c)---------
> (2)
Where
P(c|x) is the posterior probability of the
class (high-risk or low-risk) given the
predictors, calculated as (2), P(c) is the prior
probability of the class, P(x|c) is the
likelihood which is the probability of the
predictor given the class, and P(x) is the
prior probability of predictor.
5. PERFORMANCE METRICS
We measure the performance of the
classifiers with respect to different
performance metrics like precision value,
recall value, F-measure value.
Precision value (p): provides correctness
Calculate the precision with respect to a
particular class. This is defined as
Correctly classified positives
p= ------------------------------
Total predicted as positive
Recall value(r): provides completeness
Calculate the recall with respect to a
particular class. This is defined as
Correctly classified positives
r= ----------------------------------------------
Total positives
F-Measure (f): it is the harmonic mean of
precision value and recall value
Calculate the F-Measure with respect to a
particular class. This is defined as
2 * r * p
F=-- ----------------------
r + p
6 EXPERIMENTAL WORK

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This experiment have done with the help of
open source tools in window environment
using eclipse software. In this experiment,
we used the java code and libraries, which
are available in WEKA. To conduct the
experiment following procedure has to
follow.
We divide our data set into training sets and
testing sets to apply supervised learning. We
use Naive Bayes classifier to explore our
data set, primarily because previous work
has shown that these algorithms present a
good trade-off between simplicity and
accuracy. Patients are classified into one of
two classes: (i) ’diabetic’ i or (ii) ’non -
diabetic’. We use 10-fold cross validation in
training and then we apply the model onto
our testing set.
Consider 100% of data means full instances
then
For each possible subset for features (for
example if there are 8 attribute then 2^8
possible subset)Apply10-fold cross
validation for building the model then note
down the precision value, recall value, f
score value. Repeat the experiment for 90%
of data,80% of data,70% of data,60% of
data,50% of data.by conducting this
experiment we know how each feature or
combination of feature act on different size
of data.
7. RESULT ANALYSIS AND
DISCUSSION
In this paper, we examine the effect of data
size on feature set using naïve Bayes
classifier.
For each attribute set for example if there
are 8 attribute then 2^8-1=256-1=255 subset
possible. For each subset graph generated.
Which shows performance of each
The following figure shows the effect of
features(0,1,2,4) on different data size

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The below graph shows the effect of
attribute subset(2,4,5,6) on different data
size

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8. CONCLUSION AND FUTURE
WORK
The objective of this study is to evaluate
effect of data size on feature set and
investigate the performance using Naïve
Bayes algorithm based on WEKA. The
experiment shows the effect of each attribute
or combination of attribute affecting the
performance on different data size i.e. for
each possible subset of attribute affecting
the performance for prediction on different
size of data.
As a future work we can conduct same
experiment on different data set for example
:heart attack dataset and diabetes dataset
from the experiment we can combine
common attribute affect for prediction also
we can work using different classification
algorithm.
9. REFERENCES
[1] N.J.Nilsson, “Introduction to
Machine Learning,” 2010
http://ai.stanford.edu/~nilsson/mlboo
k.html
[2] M. S. Sapna and D. A. Tamilarasi,
“Fuzzy Relational Equation in
Preventing Neuropathy Diabetic,”
Internati- onal Journal of Recent
Trends in Engineering, Vol. 2, No. 4,
2009, p. 126.
[3] UCI Machine Learning Repository.
http://www.ics.uci.edu/mlearn/MLR
epository.html
[4] R. Radha and S. P. Rajagopalan,
“Fuzzy Logic Approach for
Diagnosis of Diabetes,” Information
Technology Journal, Vol. 6, No. 1,

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pp. 96-102.
doi:10.3923/itj.2007.96.102
[5] G. H. John and P. Langley,
“Estimating Continuous
Distributions in Bayesian
Classifiers,” Proceedings of the 11th
Conference on Uncertainty in
Artificial Intelligence, San
Francisco, 1995, pp. 338-345.
[6] L. Carnimeo and A. Giaquinto, “An
Intelligent System for Improving
Detection of Diabetic Symptoms in
Retinal Images,” IEEE International
Conference on Information
Technology in Biomedicine,
Ioannina, 26-28 October 2006.
[7] F. Ensan, M. H. Yaghmaee and E.
Bagheri, “Fact: A New Fuzzy
Adaptive Clustering Technique,” The
11th IEEE Symposium on Computers
and Communications, Sardinia, 26-
29 June 2006, pp. 442-447.
doi:10.1109/ISCC.2006.73

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2017