IRJET-Survey on Data Mining Techniques for Disease Prediction

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 630
Survey on data mining techniques for disease prediction
Durga Kinge1, S. K. Gaikwad2
1Student, Dept. of Computer engineering, College of Engineering Pune, Maharashtra, India
2 Professor, Dept. of Computer engineering, College of Engineering Pune, Maharashtra, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Medicinal services producesgiganticinformation
on every day ground having diverse structures like printed,
images, numbers pool and so forth. However there is absence
of devices accessible in heathcare to process this data. Data
mining frameworks are utilized to extricate information from
this data which can be utilized by media proficient individual
to figure future procedures. Heart illness is the primary driver
of death in the masses. Early recognizing and hazard
expectations are essential for patient's medicines and
specialists analysis. Data mining algorithmslikeDecisiontrees
(J48), Bayesian classifiers, Multilayer perceptron, Simple
logistic and Ensemble techniques are utilizedtodeterminethe
heart ailments. In this work, different data mining
classification procedures are analyzed for testing their
precision and execution on preparing medicinalinformational
index. The classification results will be envisioned by various
representation procedures like 2D diagrams, pie graphs, and
different techniques. The beforehand mentioned calculations
are analyzed and assessed based on their exactness, time
utilization factor, territory under ROC and so on.
Key Words: Data mining, Decision tree, Ensemble
techniques, multilayer perceptron, Bayesianclassifiers,
simple logistic.
1. INTRODUCTION
Heart ailment are one of the significant reason of death and
disability on the planet, killing 17.5 million individualsevery
year and more than twenty-three millionanticipatedpassing
from cardiovascular sickness by 2030. Coronary illness
incorporates different sorts of conditions that can influence
center reason. The heart is an important organ of human
body. On the off chance that the blood dissemination to the
body is lacking, the organs of the body that is cerebrum and
heart quit working and passing happens in couple of
minutes. The peril factors related are distinguished as age,
family history, diabetes , hypertension, elevated cholesterol,
tobacco, smoke, liquor inward breath, heftiness, physical
idleness, chest torment write and less than stellar eating
routine [1].
Medical industry is data rich yet learning poor. There is
requirement for a wise emotionally supportive network for
ailment forecast. Data mining strategies like Classification,
regression are utilized to anticipate the infection. With the
advancements of computing facility gave by software
engineering innovation, it is currently conceivable to
anticipate many states of infirmities more accurately[15].
Data mining is a cognitive procedure of discovering the
hidden approach patterns from large data set. It is generally
utilized for applications, for example, financial data ,analytic
thinking, retail, media transmission industry, genome data
analysis, logical applications and health mind frameworks
and so on. Data mining holds Extraordinary potential to
improve heath frameworks by utilizing data and analyticsto
recognize the accepted procedures that enhance care and
reduce cost. WEKA is a effective tool as it contains both
supervised and unsupervised learning techniques[14]. We
utilize WEKA because it causes us to evaluate and compare
data mining techniques (like Classification, Clustering, and
Regression etc.) conveniently on real data.
The objective of this work is to anayze the potential
utilization of classification based data miningtechniqueslike
naive bayes, decision tree(j48), ensemble algorithms and
simple logistic and so forth.
2. LITERATURE REVIEW
Various work has been improved the situation disease
forecast concentrating on heart illness utilizing different
data mining systems. Authors have connected distinctive
data mining techniques like decision trees, KNN, support
vector machine, neural network that contrast in their
accuracy, execution time.
Mr.Chintan Shah et.al [1], clarifies dialog of different
classification algorithms in view of specific parameters like
time taken to build the model, accurately and inaccurately
classified instances and so on. Theresa Princy. R. [2]
proposed a framework to precisely foresee heart disease
utiizing ID3 and KNN classifiers and accuracy level also
provided for different number of attributes.
Finding of coronary illness with the assistance of Bayesian
Network calculation has been characterized by Xue et al [3].
Abraham proposed a methodology so as to increase
classification accuracy of medical data based on NaiveBayes
classifier algorithm [4]. Palaniappan & Awang [5]
recommended a model of IHDPS (Intelligent Heart Disease
Prediction System) actualizing data mining calculations,like
Naive-Bayes, Decision Trees and Neural Network. The last
yield of these algorithm depicts that every strategy has its
distinctive capacitiesin the reason for the portrayed mining
objectives.
Jagdeep Singh impemented different association and
classification methods on the heart datasets to foresee the
heart illness. The association algorithm like Apriori and

FPGrowth are used to discover association rules of heart
dataset attributes[6]. In [7], diverse machine learning
systems including Decision Tree (DT), Naive Bayes (NB),
Multilayer Perceptron (MLP), K-Nearest Neighbor (K-NN),
Single Conjunctive Rule Learner (SCRL), Radial Basis
Function (RBF) and Support Vector Machine (SVM) have
been applied, individually and in combination, using
ensemble machine learning approaches, on the Cleveland
Heart Disease data set keeping in mind the end goal to
analyze the execution of every strategy. Gudadhe et al. [8]
realized a design base with both the MLP network and the
SVM approach. This design accomplished an accuracy of
80.41% in terms of the classification between two classes
(the presence or absence of heart disease, respectively).
Author in [9] assesses the disease categorizationusingthree
different machine learning calculations by WEKA Tool. We
compare the results in terms of time taken to build the
model and its accuracy. This work demonstratetheRandom
Forest is best classifier for disease categorization of WEKA
tool because it runs efficiently on large datasets. In paper
[10], author applied HNB classifier for analysis of coronary
illness tested execution for heart stalog data collection.
Experimental result demonstrate that HNB model exhibitsa
predominant execution compared with other Approaches.
Proposed approach applies discretization and IQR filters to
enhance the efficiency of Hidden naïve bayes.
Authors in [12] executed the framework thatextractshidden
knowledge from a historical heart disease database. Mamta
Sharma[13] uncovers that the Neural Networks with 15
attributes shows significant results over all other data
mining techniques. Decision Tree methods has proven
excellent precision with C4.5, ID3, CART and J48.
3. THEORITICAL BACKGROUND
Knowledge discovery in databases (KDD) is the interactive
and iterative process of finding valuable information from a
collection of data. KDD incorporates multidisciplinary
exercise. The means engaged with KDD process are listed
below-
Selection - Data applicable to the analysis task are retrieved
from the database
Pre-processing - In this step noise and inconsistent data are
removed from large data set. Data cleaning is a fundamental
step to solve inconsistency problem and cleanup errors in
crude data.
Transformation - The strategies like smoothing,aggregation,
normalization to transform them into formsappropriatefor
mining.
Data mining - Intelligent strategies are applied in order to
extract data patterns.
Interpretation/ Evaluation - Data patterns areevaluatedand
visualized and removing redundant patterns from the
patterns we generated.
Data mining is the core part of the knowledge discovery
process of sorting through large data sets to discover
correlation among attributes. Therearefewnoteworthydata
mining techniques have been produced and used in data
mining projects as outlined in underneathfigure.Description
methods concentrate on understanding the way the
underlying data operateswhileprediction-orientedmethods
aim to build a behavioral model for acquiring new and
unseen samples and for foreseeing estimations of at least
one variables related to the sample.
Fig -1: Taxonomy of Data Mining Methods
These strategies falls into two categories in particular
supervised and unsupervised learning. In supervised
learning a function is inferred from training data while in
unsupervised learning, find hidden structured data in
unlabeled data.
3.1 Data mining techniques used for Heart Disease
Prediction
There are two forms of analysis algorithms introduced in
data mining as classification and prediction.
3.1.1 Classification
Classification is a supervised technique which assigns items
in the collection to target category or classes. Mainly two
classes are present- binary and muti-class.
The classification task takes asinput the componentvectorX
and predicts its value for the outcome Y i.e.
C(X) ∈ Y
where:
X is a feature vector
Y is a response taking values in the set C
C( X) are the values in the set C.
It is one of a few strategies utilized for the analysis of
substantial datasets adequately. A classificationassignments
beginswith the recordswhose class labels are known. Inthe
training phase, algorithm discovers relationships between
the values of the predictors and the values of the objective.

Diverse classification algorithms utilize distinctive
techniques for discovering relationships. These connections
are summarized in a model, which would then have the
capacity to connected to an other informationalcollectionin
which the class assignments are obscure for testing reason.
3.1.2 Prediction
Regression is adapted to foresee the scope of numeric or
continuous values given a particular dataset.
Following equation demonstrate that regression is the way
toward estimating the value of a continuous target (p) as a
function (F) of one or more predictors (x1 , x2 , ..., xn), a set
of parameters (R1 , R2 , ..., Rn), and a measure of error (e).
eRxFp  ),(
Regression helps in distinguishing the behavior of a variable
when other variable(s) are changed in the process.
3.1.3 Clustering
It is unsupervised learning technique in which specific
arrangement of unlabeled occurrencesare gathered in view
of their characteristics. By representing the records
including fewer clusters loses certain fine details, but
achieves simplification. Cluster analysis expects to discover
the groups with the end goal that the inter-cluster similarity
is low and the intra-group similitude is high. There are few
distinctive methodologies of clustering: partitioning,
hierarchical, density-based, grid-based and constrained-
based methods.
3.1.4 Ensemble learning
Ensemble learning is also called committee based learning/
multiple classifier systems/ classifiercombinations.Theidea
of deploying multiple model has been utilized for long time.
In this compose multiple classifiers are consolidatedtosolve
same problem by constructing set of hypothesis and join
them to use. Training data might not provide adequate
information for choosing single best learner.Ensemblehelps
to minimize noise, bias, variance. For getting good ensemble
base learners ought to be more accurate and diverse.
Accuracy can be accomplished by cross-validation and
diversity can be achieved by sub sampling of training
examples, manipuating attributes and outputs, injecting
randomness.
Strategies used for ensemble learning are Boosting,Bagging,
Stacking. These are also called as meta algorithms.
Bagging : It is also called as bootstrap aggregation which
tries to implement similar learners on small sample
population and then takes mean of all predictions.
Equation for bagging = 
n
i
ie
n 1
1
It is parallel technique used to decrease the variance . It
trains M distinct trees on various subsets of data picked
arbitrarily with substitution and compute the ensemble. It
uses bootstrap sampling to obtain data subsets of training
the base learners.
Boosting : Boosting is an ensemble technique in which the
predictors are not made autonomously, but rather
consecutively used to reduce bias. This system utilizes the
rationale in which the subsequent predictors learn from the
mistakes of the previous predictors and finally takes
weighted average. In this way, the perceptions have an
unequal likelihood of showing up in resulting models and
ones with the highest error appear most.
Equation for boosting = 
n
i
iiew
n 1
1
In this way, the perceptions have an unequal likelihood of
showing up in resulting models and ones with the most
elevated blunder seem most.
Stacking : In this type the base level models are trained
based on a complete training set, then the meta model is
trained on the output of base level for improving predictive
force.
3.2 Algorithms used for disease prediction
3.2.1 Decision tree algorithm(J48)
It is a supervised learning algorithm used to predict class /
value of target variable using decision rules. Each inward
hub of the tree relates to an attribute, and each leaf hub
compares to a class name. The record’s attribute values are
continuously compared with other internal nodesofthetree
until leaf node is reached with predicted class value.
Fig -2 : Decision Tree
Decision Treesfollow Sumof Product (SOP) representation
for all the classes. Decision trees can handle both categorical
and numerical data. Attribute selection is based on
information gain and gini index.


c
i
ii ppSE
1
2log)(
To measure randomness/ unertainty of random variabe
entropy is utilized. Decision tree has an issue ofoverfitting,it
happens when the calculation keeps on going further and

more profound to diminish the preparation set blunder
however comes about with an extended test set mistake.J48
is an extension of ID3. Different highlights of j48 algorithm
are that it supports tree pruning, can handle missing values
and furthermore gives out efficient yield for prediction
analysis in weka[11].
3.2.2 Naive Bayes
The Naive Bayes Classifier system depends on the Bayesian
speculation and is especially suited when thedimensionality
of the input is high. BayesTheorem - It workson conditional
probability. Conditional probability is the probabilitythatan
occasion will happen, given that other occasion has just
happened.
)(
)(*)|(
)|(
EP
HPHEP
EHP 
where
P(H) is the likelihood of hypothesis H being valid. This is
known as the prior probability.
P(E) is the likelihood of the evidence(regardless of the
hypothesis).
P(E|H) is the likelihood of the evidence giventhathypothesis
is true.
P(H|E) is the likelihood of the hypothesis given that the
evidence is there.
Naive Bayes Classifier - It predicts membership
probabilities for each class, for example, the likelihood that
given record or information point has a place with a specific
class. The class with the highest probability is consideredas
the most likely class. It is a highly fast and scalablealgorithm.
3.2.3 Support Vector Machine (SVM)
A Support Vector Machine (SVM) is a supervised learning
classifier characterized by by a separating hyperplane. The
hyperplane is a line that partitions a plane in two sections
where each class lay in either side.
There are 2 sorts of SVM classifiers:
1. Linear SVM Classifier
2. Non-Linear SVM Classifier
3.2.4 Random Forest
This constructs a randomized decision tree in each iteration
of the algorithm and frequently creates excellent predictors.
Every sub tree gives a classification and provides the tree
votes for that class.
Every tree in the ensemble is built using sample from the
training set. With increase in number of trees in forest
accuracy increases too. Random forest algorithm can usefor
both classification and the regression kind of problems. For
classification issues, the ensemble of trees vote in favor of
the most prominent class. In the regression problem, their
responses are averaged to obtain an estimate of the
dependent variable.
Random Forests reduces high variance and bias present in
decision tree by averaging the values o outcomes.
3.2.4 AdaBoost
The core principle of AdaBoost is to fit a succession of feeble
learners (i.e., models that are just somewhat superior than
random guessing, such as small decisiontrees)onrepeatedly
modified versions of the data. The predictions from every
one of them are then combined through a weighted majority
vote (or sum) to deliver the final prediction.
3.2.5 Simple Linear Regression model
Simple linear regression is a statistical method that
empowers users to summarize and study relationships
between two continuous variables. It is a model that expect
a linear relationship between the input variables (x) and the
single output variable (y). Here the y can be figured from a
linear blend of the input variables (x).
If there is only one input variable (x), the method is called a
simple linear regression otherwise multiple linear
regression.
3.2.6 Multilayer Percepteron(MLP)
Multilayer perceptron whichmakes use of multiple layersof
the neural network is created by using the set of various
parameters which are selected to adjust the models withthe
help of correlation between parameters and prediction of
the disease[16].
Fig-3 : Support Vector Machine
SVMs are powerful when the quantity of highlights are very
vast. Since the SVM algorithm works locally on numeric
properties, it utilizes a z-score standardization on numeric
characteristics.
0
(.) RRf M

_
1
k 1
th
responsetreeK
k
k

Random forest prediction = 

An MLP comprises of multiple layers of nodes in a directed
graph, with each layer completely associated with the
followingone. Aside from the information nodes,everynode
is a neuron (or preparing component) with a nonlinear
enactment work. MLP utilizes a supervised learning
technique called back propagation for training the network.
Multi-layered neural networksare basically used to manage
data-sets that have an extensive number of features,
especially non-linear ones. Intuitively, the more hidden
layers it has, the more 'complex' shapes it can fit.
3.2.7 Logistic Regression
Logistic regression is a type of supervised technique that
measures the connection between the dependent and
independent variables by evaluating probabilities using a
logistic function. Logistic regression predicts the likelihood
of a result that can only have two values (i.e. a dichotomy).
In this model categorical variables are used.
3.3 Performance Metrics
3.3.1 Accuracy : The ability of the model to correctly
predict the class label of new or previously unseen data
Accuracy =
FNFPTNTP
TNTP
T
N



Where
N- number of correct predictions
T - Total of all the cases to be predicted
True positive (TP): Sick people correctly identified as sick
False positive (FP): Healthy people incorrectly identified as
sick
True negative (TN): Healthy people correctly identified as
healthy
False negative (FN): Sick people incorrectly identified as
healthy
Recall = TR/ T= TP / (TP + FN)
Specificity = TN / N = TN / (TN + FP)
Precision = TP / P = TP / (TP + FP)
F Score = 2*(Recall * Precision) / (Recall + Precision)
3.3.2 Mean Absolute Error : Measure of difference
between two continuous variables.
MAE =
n
ForecastActual
n
i
ii

1
3.3.3 Root Mean Squared error : It follows an assumption
that error are unbiased and follow a normal distribution.
RMS =
n
e
n
i
i1
2
3.3.4 ROC (Receive operating characteristic) : It is a
graphical representation of performance of classifiers.
3.3.5 Kappa Statistics : The kappa statistic is frequently
used to test inter rater reliability that is it isused tocompare
accuracy of the system to the random system.
K = P(A) − P(E) /(1− P(E)
P(A) - Agreement percentage
P(E) - Agreement chances.
If K = 1 - Agreement in tolerable range
4 DATA ANALYSIS AND RESULTS
The dataset utilized is the UCI Heart-disease dataset having
total 303 instances. It comprise of aggregate 75 attributes
from which 14 are used. Attributes utilized are of real,
binary, nominal, and ordered type. The attributes and its
descriptions are given as below
Table -1 : Heart Disease Dataset
SR NO NAME TYPE/ VALUES
1 age Real
2 sex Binary (1=M, 0=F)
3 Chest pain type Nominal (4 Values)
4 Resting blood
pressure
Real
5 Serum cholesterol Real
6 Fasting blood sugar Binary
7 Resting ECG Nominal (0,1,2)
8 Max heart rate
achieved
Real
9 Exercise enduced
angina
Binary(1= yes; 0=
no)
10 oldpeak Real
11 Slope Ordered
12 Number of major
vessels
Real
13 thal Nominal (3,6,7)
14 class Present / Absent
4.1 Interpretation and Evaluation
All tests are based on Ten-Fold Cross-Validation.Thissection
looks at the classification accuracy of the seven supervised
algorithms in particular Naïve Bayes, J48, and Random
Forest, Adaboost, Bagging, MLP, Simple Logistic.

Table -2 Supervised simulation errors
Mean
Absolute
Error
Root
Mean Sq
Error
Relative
Absolut
e Error
Root
Relative
Sq error
J48 0.25 0.43 55.48 92.59
N. Bayess 0.18 0.35 37.16 72.40
R. Forest 0.27 0.35 54.57 72.18
Adaboost 0.23 0.36 48.06 76.62
Bagging 0.29 0.37 59.41 75.95
MLP 0.21 0.42 45.54 86.44
S.logistic 0.23 0.35 46.60 71.59
As appeared in Table II and Table III, all simulations were
performed using WEKA machine learning environment
which consists of collection of popular machine learning
techniques that can be used for practical data mining
Table -3 WEKA evaluation criteria
Preci
sion
Recall F Kap
pa
Stat
Area
under
ROC
Time
in sec
J48 0.80 0.82 0.81 0.55 0.79 0.01
N Bayes 0.84 0.87 0.86 0.67 0.90 0.04
RF 0.84 0.87 0.85 0.66 0.90 0.26
Adboost 0.84 0.84 0.83 0.63 0.89 0.06
Bagging 0.83 0.85 0.84 0.63 0.88 0.08
MLP 0.82 0.81 0.81 0.58 0.86 0.59
S.logist 0.84 0.87 0.85 0.66 0.90 0.32
Classification metrics such as TP (True Positive), FP (False
Positive), Precision, Recall, FMeasure and ROCAreaareused
to assess classifiers performance.
Chart -1: Accuracy of Algorithms
Above figure shows the accuracy levels for all classifiers. It
demonstrate that both J48 and random forest,simplelogistic
have better accuracy whereas the J48 shows poor accuracy
levels.
Chart -2 : Classifier Accuracy Measures Values
5 CONCLUSIONS
Heart Disease is a fatal disease by its nature. This disease
makes a life threatening complexities, for example heart
attack and death. The significance of Data Mining in the
Medical Domain is acknowledged and steps are taken to
apply relevant techniques in the Disease Prediction. The
variousresearch workswith some effective techniquesdone
by different people were studied. This work evaluates the
disease categorization using diverse machine learning
algorithms by WEKA Tool.
In this work, seven classifiers Naive Bayes, Random Forest,
simple logistic, Bagging, MLP and Adaboost are used for
experimentation. It showsthat randomforest,simplelogistic
and Naive bayes perorm better for heart disease prediction.
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IRJET-Survey on Data Mining Techniques for Disease Prediction