Download Medical Data Mining Techniques for Health Care Systems

DOI 10.4010/2016.811
ISSN 2321 3361 © 2016 IJESC
Research Article
Volume 6 Issue No. 4
Medical Data Mining Techniques for Health Care Systems
R. Naveen Kumar 1, M. Anand Kumar 2
Research Scholar 1, Associate Professor 2
Department of Computer Science1, Department of Information Technology2
Karpagam University, Coimbatore, Tamil Nadu, India
Abstract:
Due to the sequence in the information technology, the prevalence of the healthcare organizations conserves their data
electronically. Enormous progress in medical data leads to be scarce in the mining of well-informed in series from the mass data.
There is a necessity for accomplished analysis tools to resolve covered relatives and desire in data. Data mining can represent new
biomedical and healthcare details for clinical preference. The relationship comes together from the data of patients composed in
database aid in the resultant progression. This inspection investigates the worth of a variety of Data Mining techniques such as
classification, clustering, association, regression in health domain. This paper work presents a brief preamble of the techniques that
are currently used in medical field followed by virtues and demerit of the existing techniques. This survey also highlights
applications, challenges and future issues of Data Mining in healthcare. Finally, this work provides the recommendations for
appropriate selection of available Data Mining techniques.
Keywords: Association, Classification, Clustering, Data Mining, Decision making and Healthcare.
I. INTRODUCTION
Medical data mining has been an enormous latent
process for exploring veiled patterns in data sets of medical
sphere. In healthcare, despite the fact that data mining is not
broadly used, its reputation now increasingly accepted in the
medical datasets for its earlier discovery progression. Data
mining can perk up Decision-making by discovering patterns
and trends in large amounts of multifaceted data. There are
two chief goals of data mining-prediction and portrayal.
Prediction involves some variables or fields in the data set to
envisage mysterious or future values of other variables of
curiosity. On the other hand narrative focuses on verdict
patterns recitation of the data that can be interpreted by
humans. The data generated by the health organizations is
very immense and multifaceted due to which it is
complicated to investigate the data, in order to make
significant announcement concerning patient's health. This
data contains details regarding hospitals, patients, medical
assert, cure cost etc. So, there is a must to create a
commanding tool for scrutinizing and extracting significant
information from this intricate data. The analysis of health
data improves the healthcare by enhancing the concert of
patient organization tasks. The results created by Data
Mining technologies improve the progression of predicting
the comparable patients and clustering them under a
challenging group based on illness or fitness issues, so that
healthcare involvement offers them effectual treatments. It
can also be constructive for forecasting the span of life of
patients in hospital, for medical investigation and
constructing plan for effectual information system
management. Current technologies are used in medical field
to augment the medical services in cost valuable manner.
Data Mining techniques are also used to investigate the
assortment of factors that are accountable for diseases such
as food type, diverse working situation, learning level,
livelihood conditions, accessibility of pure water, health care
services, artistic, ecological and farming factors[1].
The rest of this paper is organized as follows: Section 2 reviews the related work on data mining techniques on
Parkinson’s disease; Section 3 - presents the finding from the
survey of Heart disease Diagnosis-A and Brest Cancer
Diagnosis-B using mining techniques; Section 4 - discusses
the survey on Diabetes Disease Diagnosis using data mining
technique; Section 5 - presents the summary of the problem;
Section 6 - presents the statement of the problem; Finally,
Section 7 - concludes the work presented in this paper.
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II. DATA MINING FOR DISEASE DIAGNOSIS
With the fast increase in population, there is a substantial
quantity of augmentation in the health linked diseases.
Numerous diseases are strongly associated with a symptom
which makes it complicated for the doctors to forecast the
precise diseases on one go. This is where data mining
appears into backing; it helps in forecasting the disease
which is almost perfect. Even-though the forecasting is not
extremely accurate, it gives the doctor a concise idea what
the disease might be. Thus, Data mining is not a substitution
to doctors as an alternative, it is a accolade to them to
envisage the diseases in advance stages. [2][3]
A. PARKINSON’S DISEASE:
In the Parkinson’s illness is a neurodegenerative ailment, the
malady impinges on by brain cells (neurons) in human brain.
The neurons make a chief chemical called dopamine. The
dopamine send signal to the fraction of brain that pedals
travel. The petite signals can aid those parts of the brain
work enhanced. The decrease of dopamine in the brain
makes the person immobile. The four types of symptoms of
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Parkinson’s bug are: tremor, rigidity, Bradykinesia and
postural volatility.
When quiver occurs, it pulse by hands, arms, legs or jaws.
The sign of firmness makes limbs and trunk rigid.
Bradykinesia is a sign which leads to sluggish travels.
Postural volatility causes gloominess and poignant changes.
The basic indicator affects 75-90% of citizens with
Parkinson’s bug. The work in [4] obtained through tellmonitoring dataset, by UCI Irvine machine erudition
repository. The dataset comprises two object curriculums
(i.,e) Motor-UPDRS and Total-UPDRS. This vocation only
concentrates on attribute relevancy and not on idleness, and
the time intricacy is highly compared.
Peyman Mohammadi et al [5] obtained their revise on
medical verdict by separating 11 data mining algorithms into
five groups, which are practical to a dataset of patient’s
irrefutable variables data with Parkinson’s bug (PD), to
study the ailment string. The dataset includes 22 properties
of 42 citizens, that all of our algorithms are functional to this
dataset. The verdict table with 0.9985 association
coefficients has the finest accurateness and Decision Stump
with 0.7919 correlation coefficients has the least accuracy.
The work of Hariganesh S et al [6] discusses the Parkinson’s
disease of remote tracking used by eleven techniques with
4406 training dataset and 1469 data of test set. The elevated
accuracy of correlation coefficient in dataset is 99.85% and
99.67 % produced by M5Rules algorithm.
The research work in [6] developed the voice dimensions of
disease chiefly spotlights the speech signals [6]. The
Parkinson dataset is a series of biomedical voice
measurements from 31 people, 23 trait features in
Parkinson’s disease. The error rate of confusion matrix of
2*2 matrix is the output. The major objective is to get the
lowest amount of error rate with the minimum characteristic
of Parkinson's dataset. The random tree gives 100%
accuracy with zero error rates. Shianghan et al [7] offered
three models to investigate the Parkinson’s disease for error
probability premeditated by logistic regression analysis,
decision tree analysis and NN analysis. Error probability of
5.15% is produced by logistic regression and neural network
exhibits 23.73%. At last, the neural net analysis holds the
highest error probability and is concluded as the best
analysis in Parkinson’s disease.
The work [8] was presented in the speech of vocal sound
examination for the Parkinson’s disease patients to be
evaluated by the health control (HC) people. The speech was
assessed for four features like NHR, SPLD, RFPC and F0
SD. The disease pretentious person speaks through
microphone. The voice test for vocal task will be performed
by speech subsystem measures calculated by correct
assessment rate. The categorization accurateness is almost
85%.
The work [9] was urbanized to be evaluated for the
performance of Artificial Neural Networks (ANN) and
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Support Vector Machines (SVM). The ANN and SVM are
designed by metrics like accurate, truly positive, false
positive, positive predictive value and negative predictive
value. The authors in [10] utilized ANN which has two type
of MLP. They are: back- propagation learning and Radial
Basis Function classifier, to produce the highest accuracy of
data set. The MLP network has accuracy output with 93.22%
and RBF has accuracy output with 86.44%. In [11] the
authors proposed a speech recognized model by Melfrequency cepstral coefficients (MFCC) and Vector
Quantization (VQ). The MFCC is developed through speech
analysis using signals and frequent domain. Code book is
calculated using VQ. 20 phonations are used for normal
speech and patient with Parkinson’s disease. Finally, VQ
consequence with the codebook in normal speech of rate is
90% and patient with PD is 95%.
In [12] it presents the boosting committee, a machine which
has been developed. Boosting is a kind of filtering technique.
It is used for the neural networks with back propagation.
They mainly work on voting for the proposal. They are the
three types of experts in the preparation of set. The bulk
appointments amplify the recital tempo in excessive data set.
The highest positive rate in NN is 74%.The obtainable work
chiefly concentrates on the discovering of regulations from
the obtainable set of patterns but fall short to grip the data
preprocessing specifically.
III. A - SURVEY ON HEART DISEASE DIAGNOSIS
USING MINING TECHNIQUES
This piece presents the literature survey associated to heart
bug dataset forecast by means of data mining techniques for
judgment & achieved dissimilar probabilities for diverse
methods as discussed below.
 A gifted Heart bug prophecy System (IHDPS) urbanized
by via data mining techniques. Immature Bayes, Neural
Network, and conclusion Trees was projected in the
vocation. The effort has its own might to get suitable
fallout. To erect this system veiled patterns and
affiliation were used. It is web-based, user friendly &
stretchy.



To widen the multi-parametric trait with linear and
nonlinear (HRV - Heart Rate Variability) a narrative
practice was projected by authors [15]. To attain this,
they have used classifiers like Bayesian Classifiers,
Classification based on Multiple Association Rules,
C4.5 and Support Vector Machine.
The guess of Heart bug, Blood heaviness and Sugar with
the support of neural networks was projected by the
novelist of [14]. The dataset contains records with 13
attributes in each trace. The supervised networks i.e.
Neural Network with back broadcast algorithm is used
for preparation and difficult data.
The crisis of identifying unnatural organization
regulations for heart bug guess was deliberate by Carlos
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Ordonez [16]. The ensuing dataset contains records of
patients having heart ailment. Three constraints were
introduced to diminish the number of patterns [15].
They are as follows: 1. the attributes have to emerge on
only one side of the regulation. 2. Split the attributes
into groups. i.e. tedious groups. 3. In a decree, there
must be limited number of attributes. The consequence
of this is two groups of rules, the occurrence or
deficiency of heart bug.

The vocation in [17] builds a verdict tree with database
of enduring for a medical problem
B. SURVEY ON BREAST CANCER DIAGNOSIS
The work [39] had discussed that arithmetic neural networks
can be used to execute breast cancer decision effectively.
The researcher has compared algebraic neural network with
Multi Layer Perceptron on WBCD database. Radial basis
function, universal decline Neural Network and Probabilistic
Neural Network were used for organization and their on the
whole routine were 96.18% for Radial Basis Function, 97%
for PNN, 98.8% for GRNN and 95.74% for MLP. This work
used statistical neural network structures applied to diagnose
breast cancer to improve the detection rate considerably. Xin
Yao [40] et al. had attempted to execute neural network for
breast cancer diagnosis. Negative correlation training
algorithm was used to molder a trouble routinely and resolve
them. This article has discussed two approaches such as
evolutionary approach and ensemble approach, in which
evolutionary approach can be used to devise compact neural
network involuntarily.
The ensemble approach was aimed to undertake bulky
problems but it was in advancement. The works [38] have
done an examination on data mining techniques for gene
selection classification. This article is in conformity with the
majority used data mining techniques for gene selection and
cancer classification. Mainly they have four major rising
fields as Neural network, Machine learning, genetic and
cluster based techniques which are specified for the future
improvement.
The work [41] have presented an article on an intelligent
system for automated breast cancer diagnosis and prognosis
using SVM based classifiers with Bayesian classifiers and
ANN for prognosis and diagnosis of breast cancer disease.
Wisconsin diagnostic breast cancer datasets were used to
employ SVM model to offer division between the malignant
and benign breast masses. These datasets involve quantity
taken according to Fine Needle Aspirates. An inflammation
felt during the assessment approximately gives evidences to
the size of tumour and its texture. In research work [19] a
comparison is performed on neural network , MLP, PNN ,
SOM and RBF to classify the presence of heart disease using
WBC and NHBCD data. According to this work RBF had
produced its best only in training set whereas PNN had
proved its best performance both in training and testing set.
The work demonstrated that statistical neural networks can
be efficiently used for breast cancer diagnosis. By pertaining
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several neural network structures a diagnostic scheme was
performed fairly.
The work [20] explored the applicability of decision trees for
the exposure of high-risk breast cancer groups. The dataset
produced by the Department of Genetics of faculty of
Medical Sciences with 164 controls and 94 cases. To
statistically legalize the association found, incarnation tests
were used. They establish a high-risk breast cancer group
unruffled of 13 cases and only 1 control, with a Fisher Exact
Test which is used for validation value of 9.7*10-6 and a pvalue of 0.017. These consequences showed that it is
probable to discover statistically important relations with
breast cancer by obtaining a decision tree and picking the
finest folio. The work [21] examined the ability of the
classification SVM with Tree Boost and Tree Forest. In
analyzing the DDSM dataset, the mining mammographic
accumulation features categorize true and false cases.
Increasing diagnostic accuracy is shown promising by SVM
witnessed by the largest area under the ROC curve
comparable to values for tree boost and tree forest.
The work [22] explored that the genetic algorithm model
yielded better results for the detection of breast cancer
patient classification along with the expression and
complexity of the classification rule. WBC dataset is used
for analyze and it is performed by artificial neural network,
decision tree, logistic regression, and genetic algorithm. The
result reveals that the accuracy and positive predictive value
of each algorithm were used as the evaluation metric and
finally genetic algorithm produce more acceptable accuracy.
The work [23] constructed classification rules use the
Particle Swarm Optimization Algorithm for breast cancer
datasets. This paper handles the feature subset selection as a
preprocessing step which infers the rule using fuzzy logic
with genetic algorithm and the selected features dataset are
used for classification rules developed by PCO and the result
shows the rate of accuracy defining the underlying attributes
effectively.
The research work [24] performed a comparative study on
WBC dataset for breast cancer prediction using RBF and
MLP along with logistic regression. When comparing RBF
and MLP neural network models, it was found that RBF had
good predictive capabilities and also the time taken by RBF
was less. The hierarchical granulation based rough set theory
was proposed in the work [25]. This method adopted to
determine the rules with minimal attributes using lower
approximations of rough set theory. The simulation is done
on WBC dataset and the result analysis showed that the
proposed work produced effective result on classification.
The paper [26] presented a novel approach by performing
reduced dimensionality and normalizing of the WBC data
with 360 instances. The preprocessed data is then observed
by a rough set method for generating classification rules. To
optimize the generated rules the rough set reduction
technique was applied to find all reducts of the data which
contains the minimal subset of attributes and determined
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optimized 30 rules out of 472.The paper [27] applied SVM
and ANN on the WBC data .The results of SVM has 97%
accuracy while comparing ANN prediction models which
hold 89. This work mainly contributes to the decision
making for biopsy.
The work [28] presented advancement for early breast
cancer diagnosis by integrating ANN and multiwavelet
based sub band image decomposition. The MIAS dataset
was used for testing the proposed approach was tested using
the MIAS mammographic databases and the result shows
that the multiwavelet performs better with areas ranging
around 0.96 under ROC curve. The proposed approach could
aid the radiologists in mammogram psychoanalysis and
analytic verdict making.
IV. DIABETES DISEASE DIAGNOSIS USING DATA
MINING TECHNIQUE
A good number of researches have been reported in
literature on diagnosis of diabetes disease diagnosis. This
section deals with discussion of some related work in
diabetes disease diagnosis using data mining technique.The
work in [29] proposed by the author based on neuropathy
diabetics which is a kind of nerve disorder due to diabetic
mellitus. This is mainly affected by the long term diabetics.
This paper deals with the symptoms and risk factors of
neuropathy taken into the consideration for deployment of
fuzzy based relation equation. It is linked with the Multilayer
perceptron in composition of binary relation using fuzzy
inference model.
The authors of [30] devised an automatic retinal diabetic
detection using a multilevel perceptron neural network. To
evaluate the optimal global threshold to minimize the pixel
classification errors, the network is trained by this algorithm.
The performance of the proposed work improves by the
detection and adequate index based on neuro fuzzy
subsystem. The fuzzy set and linguistic variable are used in
[31] to diagnose diabetes. This paper used the maximum and
minimum relationship to deal with uncertainty availed in the
dataset. Data sets of forty patients were collected to produce
this relationship.
to original fuzzy ARTMAP and other machine learning
systems.
The authors in [35] proposed a fuzzy clustering technique
which determined the number of appropriate clusters based
on the prototype spirit. Diverse experiments for algorithm
evaluation were executed, which showed an improved
presentation to the distinctive widely used K-means
clustering algorithm and data was taken from the UCI
Machine Learning Repository [36]. In the work [37]
proposed a new SSVM for classification of diabetes patient
problems was considered. It is called Multiple Knot Spline
SSVM (MKS-SSVM). To assess the usefulness of their
method, they carried out a research on Pima Indian diabetes
dataset. First, hypothetical of MKS-SSVM was obtainable.
Then, application of MKS-SSVM and assessment with
SSVM in diabetes disease diagnosis was agreed. The results
of this study demonstrated that the MKS-SSVM was
effectual to perceive diabetes disease diagnosis.
V. SUMMARY
The survey reveals that there are lots of existing
techniques available for detection of disease diagnosis by
performing classification or clustering techniques. But still
there is a low concentration on the quality of the dataset,
when it is incomplete. There are very few papers which work
on data preprocessing, feature selection and reduction.
VI. STATEMENT OF THE PROBLEM
The existing techniques used for disease diagnosis
are still suffering from false detection rate, due to the raw
nature of dataset. The insufficient and incomplete dataset
may lead to false alarms in diagnosis of accurate results. The
preprocessing of dataset is less concentrated in the existing
work, which leads to major setback in the overall process.
The future work of this survey will start with a data
preprocessing technique to produce a complete dataset
instead of using raw dataset.
In the work [33] fuzzy membership function is used in
connection with fuzzy neural network to detect the diabetes
in early stages. This work uses two experimental
examinations for medical data. In the paper [34] developed a
substitute pruning technique based on the Minimal
Description Length principle. It can be viewed as
substitutions between theory complexity and data prediction
accuracy. This work proposed a greedy search algorithm to
prune the fuzzy ARTMAP categories one by one. The results
proved that fuzzy ARTMAP pruned with the MDL principle
gave improved recital with less categories shaped compared
VII. CONCLUSION
Over the past few decades, the automated problemsolving tools have been intended to assist the physician with
a clear sense of medical data. In healthcare, data mining is
becoming increasingly more essential. From the above study
it is examined that the support vector machine due to its
regularization parameter is often recommended by a lot of
the researches to evade over-fitting. With the help of its
kernel trick it can box to build an expert inference system. In
clustering models k-means based forecasting and variants
of it also produce promising results. In evolutionary based
models, particle swarm optimization with its variants
contributed more innovatively in many disease diagnosis
works. It also assumes the real number code, and it is
determined honestly by the solution. The selection of data
mining approaches depends on the nature of the dataset. If
the dataset consists of the labeled features then the
classification techniques can be suggested for best
prediction. If the dataset is with unlabelled features then the
clustering techniques are most excellently suited for pattern
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In paper [32] presented a binary classification based
neural network techniques used diabetes diagnosis problem.
The assessment is done based on three benchmark data sets
obtained from UCI machine learning repository.
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recognition. If the optimization of the results needs to be
improvised, bio inspirational based techniques are best
suited. Still the diagnosis of disease suffers from high false
alarm and detection rate is low. In the future work it is
planned to propose a novel approach to reduce the false
alarm rate in the situation of incomplete dataset handling.
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1.
3503
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