Review of heart disease prediction system using data mining and hybrid intelligent techniques

Abstract

The Healthcare industry generally clinical diagnosis is done mostly by
doctor’s expertise and experience. Computer Aided Decision Support
System plays a major role in medical field. With the growing research
on heart disease predicting system, it has become important to
categories the research outcomes and provides readers with an
overview of the existing heart disease prediction techniques in each
category. Neural Networks are one of many data mining analytical
tools that can be utilized to make predictions for medical data. From
the study it is observed that Hybrid Intelligent Algorithm improves the
accuracy of the heart disease prediction system. The commonly used
techniques for Heart Disease Prediction and their complexities are
summarized in this paper.

Full text

ISSN: 2229-6956(ONLINE) ICTACT JOURNAL ON SOFT COMPUTING, JULY 2013, VOLUME: 03, ISSUE: 04
605
REVIEW OF HEART DISEASE PREDICTION SYSTEM USING DATA MINING AND
HYBRID INTELLIGENT TECHNIQUES
R. Chitra1 and V. Seenivasagam2
1Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, India
E-mail: jesi_chit@yahoo.co.in
2Department of Information Technology, National Engineering College, India
E-mail: yespee1094@yahoo.com
Abstract
The Healthcare industry generally clinical diagnosis is done mostly by
doctor’s expertise and experience. Computer Aided Decision Support
System plays a major role in medical field. With the growing research
on heart disease predicting system, it has become important to
categories the research outcomes and provides readers with an
overview of the existing heart disease prediction techniques in each
category. Neural Networks are one of many data mining analytical
tools that can be utilized to make predictions for medical data. From
the study it is observed that Hybrid Intelligent Algorithm improves the
accuracy of the heart disease prediction system. The commonly used
techniques for Heart Disease Prediction and their complexities are
summarized in this paper.
Keyword:
Neural Network, Hybrid Intelligent Algorithm, Heart Disease
Prediction, Computer Aided Decision Support System
1. INTRODUCTION
Heart Diseases remain the biggest cause of deaths for the last
two decades. Recently computer technology and machine
learning techniques to develop software to assist doctors in
making decision of heart disease in the early stage. The
diagnosis of heart disease depends on clinical and pathological
data. Heart disease prediction system can assist medical
professionals in predicting heart disease status based on the
clinical data of patients. In biomedical field data mining plays an
essential role for prediction of diseases In biomedical diagnosis,
the information provided by the patients may include redundant
and interrelated symptoms and signs especially when the
patients suffer from more than one type of disease of the same
category. The physicians may not able to diagnose it correctly.
Data mining with intelligent algorithms can be used to tackle
the said problem of prediction in medical dataset involving
multiple inputs. Now a day’s Artificial neural network has been
used for complex and difficult tasks. The neural network is
trained from the historical data with the hope that it will discover
hidden dependencies and that it will be able to use them for
predicting. Feed forward neural networks trained by back-
propagation have become a standard technique for classification
and prediction tasks.
The healthcare industry collects huge amounts of healthcare
data and that need to be mined to discover hidden information
for effective decision making. Discover of hidden patterns and
relationships often go unexploited [6].
Clinicians and patients need reliable information about an
individual’s risk of developing Heart Disease. Ideally, they
would have entirely accurate data and would be able to use a
perfect model to estimate risk. Such a model would be able to
categorize people with heart disease and others. Indeed, the
perfect model would even be able to predict the timing of the
disease’s onset. The risk factors for heart disease can be divided
into modifiable and non modifiable. Modifiable risk factors
include obesity, smoking, lack of physical activity and so on.
The non modifiable risk factors for heart disease are like age,
gender, and family history. Many people have at least one heart
disease risk factor.
Some kinds of heart disease are cardiovascular diseases,
heart attack, coronary heart disease and Stroke. Stroke is a type
of heart disease it is caused by narrowing, blocking, or
hardening of the blood vessels that go to the brain or by high
blood pressure [12, 13]. The rest of the paper is organized as
follows. Section 2 describes the heart disease prediction system
using data mining techniques and the intelligent and hybrid
technique with feature subset selection are discussed in section 3
and 4 respectively.
2. HEART DISEASE PREDICTION USING
DATAMINING
In this section the demining systems used for the
classification of heart disease is analyzed.
N. Deepika et al. proposed Association Rule for
classification of Heart-attack patients [1]. The extraction of
significant patterns from the heart disease data warehouse was
presented. The heart disease data warehouse contains the
screening clinical data of heart patients. Initially, the data
warehouse preprocessed to make the mining process more
efficient. The first stage of Association Rule used preprocessing
in order to handle missing values. Later applied equal interval
binning with approximate values based on medical expert advice
on Pima Indian heart attack data. The significant items were
calculated for all frequent patterns with the aid of the proposed
approach. The frequent patterns with confidence greater than a
predefined threshold were chosen and it was used in the design
and development of the heart attack prediction system. The,
Pima Indian Heart attack dataset used was obtained from the
UCI machine learning repository. Characteristics of the patients
like number of times of chest pain and age in years were
recorded. The actions comprised in the preprocessing of a data
set are the removal of duplicate records, normalizing the values
used to represent information in the database, accounting for
missing data points and removing unneeded data fields.
Moreover it might be essential to combine the data so as to
reduce the number of data sets besides minimizing the memory
and processing resources required by the data mining algorithm
[15]. In the real world, data is not always complete and in the

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case of the medical data, it is always true. To remove the number
of inconsistencies which are associated with data we use Data
preprocessing.
K. Srinivas et al. presented Application of Data Mining
Technique in Healthcare and Prediction of Heart Attacks [2].
The potential use of classification based data mining techniques
such as Rule based, Decision tree, Naïve Bayes and Artificial
Neural Network to the massive Volume of healthcare data.
Tanagra data mining tool was used for exploratory data analysis,
machine learning and statistical learning algorithms. The
training data set consists of 3000 instances with 14 different
attributes. The instances in the dataset are representing the
results of different types of testing to predict the accuracy of
heart disease. The performance of the classifiers is evaluated and
their results are analyzed. The results of comparison are based
on 10 tenfold cross-validations. According to the attributes the
dataset is divided into two parts that is 70% of the data are used
for training and 30% are used for testing. The comparison made
among these classification algorithms out of which the naive
Bayes algorithm considered as the best performance algorithm.
The performance of various algorithms is listed below [2].
Table.1. Performance Study of Data mining Algorithms
The algorithm used
Accuracy
Time taken
Naïve Bayes
52.33%
609ms
Decision list
52%
719ms
K-NN
45.67%
1000ms
Diagnosis of heart disease was used Naïve Bayes, K-NN,
Decision List in this Naïve Bayes has taken a time to run the
data for accurate result when compared to other algorithms.
Sudha et al. [11] to propose the classification algorithm like
Naïve Bayes, Decision tree and Neural Network for predicting
the stroke diseases. The classification algorithm like decision
trees, Bayesian classifier and back propagation neural network
were adopted in this study. The records with irrelevant data were
removed from data warehouse before mining process occurs.
Data mining classification technology consists of classification
model and evaluation model. The classification model makes use
of training data set in order to build classification predictive
model. The testing data set was used for testing the classification
efficiency. Then the classification algorithm like decision tree,
naive Bayes and neural network was used for stroke disease
prediction. The performance evaluation was carried out based on
three algorithms and compared with various models used and
accuracy was measured. While comparing these classification
algorithms, the observation shows the neural network
performance was more than the other two algorithms.
M A. Jabbar et al. proposed Association Rule mining based
on the sequence number and clustering for heart attack
prediction [16]. The entire database is divided into partitions of
equal size. The dataset with 14 attributes was used in that work
and also each cluster is considered one at a time for calculating
frequent item sets. This approach reduces main memory
requirement. To predict the heart attack in an efficient way the
patterns are extracted from the database with significant weight
calculation. The frequent patterns having a value greater than a
predefined threshold were chosen for the valuable prediction of
heart attack. Three mining goals were defined based on data
exploration and all those models could answer complex queries
in predicting heart attack.18].
Mai Shouman, et al. [21] proposed k-means clustering with
the decision tree method to predict the heart disease. In their
work they suggested several centroid selection methods for k-
means clustering to increase efficiency. The 13 input attributes
were collected from Cleveland Clinic Foundation Heart disease
data set. The sensitivity, specificity, and accuracy are calculated
with different initial centroids selection methods and different
numbers of clusters. For the random attribute and random row
methods, ten runs were executed and the average and best for
each method were calculated. When comparing integrating k-
means clustering and decision tree with traditional decision tree
applied previously on the same data set, integrating k-means
clustering with decision tree could enhance the accuracy of
decision tree in diagnosing heart disease patients. In Addition,
integrating k-means clustering and decision tree could achieve
higher accuracy than the paging algorithm in the diagnosis of
heart disease patients. The accuracy achieved was 83.9% by the
enabler method with two clusters.
3. HEART ATTACK PREDICTION USING
INTELLIGENT TECHNIQUES
In this section the role of intelligent technique such as neural
network for heart disease prediction is explained
Latha Parthiban and R. Subramanian presented Intelligent
Heart Disease Prediction System using CANFIS and Genetic
Algorithm [4]. Adaptable based fuzzy inputs are adapted with a
modular neural network to rapidly and accurately approximate
complex functions. The CANFIS model combined the neural
network adaptive capabilities and the fuzzy logic quantitative
approach then integrated with genetic algorithm to diagnosis the
presence of the disease. Coactive neuro-fuzzy inference system
model has good training performance and classification
accuracies. Dataset of heart disease was obtained from UCI
Machine Learning Repository .Coactive Neuro-fuzzy modeling
was proposed as a dependable and robust method developed to
identify a nonlinear relationship and mapping between the
different attributes.
Dangare et al. proposed Improved Study of Heart Disease
Prediction System using Data Mining Classification Techniques
[6]. Prediction System for heart disease used system contains
huge amount of data, used to extract hidden information for
making intelligent medical diagnosis. The main objective of this
research was to build Intelligent Heart Disease Prediction
System that gives diagnosis of heart disease using historical
heart database. To develop the system, medical terms such as
sex, blood pressure, and cholesterol like 13 input attributes are
used. To get more appropriate results, two more attributes i.e.
obesity and smoking, as attributes were considered as important
attributes for heart disease. A Multi-layer Perceptron Neural
Networks (MLPNN) that maps a set of input data onto a set of
appropriate. It consists of 3 layers input layer, hidden layer &
output layer. There is connection between each layer & weights
are assigned to each connection. The primary function of
neurons of input layer is to divide input into neurons in hidden
layer. The dataset consists of total 573 records in heart disease

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database. The total records are divided into two data sets one is
used for training consists of 303 records & another for testing
consists of 270 records. Initially dataset contained some fields,
in which some value in the records was missing. These were
identified and replaced with most appropriate values using
Replace Missing Values filter. The Replace Missing Values
filter scans all records & replaces missing values with mean
mode method known as Data Preprocessing. After pre-
processing the data, data mining classification techniques such
as Neural Networks, is used for classification. Many problems in
business, science, industry, and medicine can be treated as
classification problems. Owing to the wide range of applicability
of ANN and their ability to learn complex and nonlinear
relationships including noisy or less precise information, neural
networks are well suited to solve problems in biomedical
engineering. So here use for the neural network technique is
classification of medical dataset 14 attributes by considering the
single and multilayer neural network models [19].
Olatubosun Olabode et al. [22] to classify the
Cerebrovascular disease by using artificial neural network with
back propagation error method. The Multi-layer perceptrons
artificial neural networks with back-propagation error method
were feed-forward nets with one or more layers of nodes
between the input and output nodes. These additional layers
contain hidden units or nodes that were not directly connected to
both the input and output nodes. The neural network was trained
using back propagation algorithm with sigmoid function on one
hidden layer with the 16 input attributes. Predictive models were
used in variety of domains for the diagnosis. Dataset for this
work were collected 100 records (60 males and 40 females) from
federal medical fields. The input values obtained from the
records of the forms the input variables in the input layer with 16
nodes. The neural network weights were initialized randomly.
This work range of the weights was between [-0.5 and 0.5] and
the learning rate was set between 0.1 and 0.9. The training,
validation, generalization accuracy was measured.
4. HEART DISEASE WITH FEATURE SUBSET
SELECTION
Feature subset selection is one of the technique used for
dimensionality reduction and thus to reduce the complexity of
the algorithm. But the selection of appropriate feature is
challenging one. In this section the heart disease prediction
system with evolutionary feature selection is explained.
M. Anbarasi et al. proposed Enhanced Prediction of Heart
Disease with Feature Subset Selection using Genetic Algorithm
[5]. Originally 13 attributes involved in prediction of heart
disease, proposed enhanced prediction of heart disease with
feature subset selection using genetic algorithm using 10
attributes for predicting and data mining techniques after
incorporating feature subset selection with high model
construction time. Classification techniques are Naïve Bayes,
Decision Tree and Classification by clustering. The genetic
search starts with zero attributes, and an initial population with
randomly generated rules. Based on the idea of survival of the
fittest, new population is constructed to comply with fittest rules
in the current population, as well as offspring of these rules.
Feature Extraction is the process of detecting and eliminating
irrelevant, weakly relevant or redundant attributes or dimensions
in a given data set. The goal of feature selection is to find the
minimal subset of attributes such that the resulting probability
distribution of data classes is close to original distribution
obtained using all attributes. The reduced data set fed to three
classification models. K fold cross validation method is used as
the test mode. Genetic algorithm is used to determine the
attributes which contribute more towards the diagnosis of heart
ailments which indirectly reduces the number of tests which are
needed to be taken by a patient. The 13 attributes are reduced to
6 attributes using genetic search. Subsequently, three classifiers
like Naive Bayes.
D. Shanthi, et al. [7] proposed to functional model of ANN
to aid existing diagnosis methods. The Back propagation
algorithm was used to train the ANN architecture and the same
had been tested for the various categories of stroke disease. The
data for this study had been collected from 50 patients who had
symptoms of stroke disease. The data had standardized so as to
be error free in nature. All the fifty cases were analyzed after
careful scrutiny with the help of the Physicians. Data were
analyzed in the dataset to define column parameters and data
anomalies. Data analysis information needed for correct data
preprocessing. After data analysis, the values had been identified
as missing, wrong type values or outliers and which columns
were rejected as unconvertible for use with the neural network.
In this study, backward stepwise method was used for input
feature selection. The removal of insignificant inputs had
improved the generalization performance of a neural network.
This method begins with all inputs and it works by removing
one input at each step. At each step, the algorithm finds an input
that least deteriorates the network performance and becomes the
candidate for removal from the input set. The architecture of the
neural network used in this study was the multilayered feed-
forward network architecture with 20 input nodes, 10 hidden
nodes, and 10 output nodes. The number of input nodes was
determined by the finalized data; the number of hidden nodes
was determined through trial and error; and the numbers of
output nodes were represented as a range showing the disease
classification. The most widely used neural-network learning
method is the BP algorithm. Learning in a neural network
involves modifying the weights and biases of the network in
order to minimize a cost function.
In this prediction of combinations of several targets attributes
for intelligent and effective heart attack prediction using data
mining. For predicting heart attack, significantly 15 attributes
are listed and with basic data mining technique other approaches
e.g. ANN, Time Series, Clustering and Association Rules, soft
computing approaches etc. can also be incorporated and
performance were analyzed. In compare to the performance of
predictive data mining technique on the same dataset and the
outcome reveals that Decision Tree outperforms and some time
Bayesian classification is having similar accuracy as of decision
tree but other predictive methods like KNN, Neural Networks,
Classification based on clustering are not performing well. The
second conclusion is that the accuracy of the Decision Tree and
Bayesian Classification further improves after applying genetic
algorithm to reduce the actual data size to get the optimal subset
of attribute sufficient for heart disease prediction [17].

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The Application of Artificial Neural Network (ANN) can be
time-consuming due to the selection of input features for the
Multi Layer Perceptron. The number of layers, number of
neurons in each layer was also determined by the input
attributes. Reducing the dimensionality, or selecting a good
subset of features, without sacrificing accuracy, was of great
importance for neural networks to be successfully applied to the
area. D. Shanthi [5] propose a neuro-genetic approach to feature
selection in disease classification.
Fig.1. Architecture of MLP
The Fig.1 shows schematically a typical representation of a
MLP with input neurons, four hidden neurons, and one output
neuron. Each of the input neurons connects to each of the hidden
neurons, and each of the hidden neurons connects to the output
neuron. The Back propagation algorithm, in particular,
adaptively changes the internal network free parameters based
on external after trained, a neural network can make predictions
about the membership of every test example. MLP is trained
with the Back propagation algorithm suffers from the high
number of parameters that need to be tuned, like learning rate,
number of neurons, momentum rate, etc. However, the
motivations to select this algorithm arise after observing that
they had been used to solve problems in different domains,
moreover, the output can be directly used for ranking purposes.
The Back propagation algorithm, in particular, adaptively
changes the internal network free parameters based on external
stimulus. After trained, a neural network can make predictions
about the membership of every test example. MLP was trained
with the Back propagation algorithm suffers from the high
number of parameters that need to be tuned, like learning rate,
number of neurons, momentum rate, etc. However, the
motivations to select this algorithm arise after observing that
they have been used to solve problems in different domains,
moreover, the output can be directly used for ranking purposes.
The fuzzy neuro expert system with genetic feature reduction
is used for diagnosis of heart disease [20].This system will help
the doctors to arrive at a decision about the presence or absence
of heart disease in patients.
5. DISCUSSION
The numerous heart attack predicting system techniques
presented in this paper. In this paper Heart attack prediction
system methodology is categorized in three types. At first type
data mining technique (mainly classification technique) are
analyzed. The second type intelligent techniques used for heart
disease prediction are analyzed. The final type the role of feature
subset in the heart disease prediction is discussed.
In data mining approach the heart disease data warehouse
contains the screening clinical data of heart patients used for
heart disease diagnosis. The classification technique is used in
all proposed work.
In intelligent technique neural network is used for disease
prediction. The MLFFNN with back propagation algorithm is
proposed in many papers discussed in this section. The main
drawback of this system is training time and complexity .The
offline training of the neural network can be advised to reduce
the time complexity. In the final model feature subset selection
only the more significant attributed are extracted to predict
accurate result. Data mining techniques combined with
intelligent and evolutionary computation are discussed in the
reviewed paper. From the result it is observed that the accuracy
of the system improved with the Feature subset selection. But in
this technique also time complexity is high. Selection of the
algorithm for feature reduction is still challenging.
In many papers author used the dataset of Heart disease was
obtained from UCI Machine Learning Repository, University of
California. Hence it might be a good choice for training the
network.
6. CONCLUSION
Heart disease is one of the leading causes of death worldwide
and the early prediction of heart disease is important. The
computer aided heart disease prediction system helps the
physician as a tool for heart disease diagnosis. Some Heart
Disease classification system is reviewed in this paper. From the
analysis it is concluded that, data mining plays a major role in
heart disease classification. Neural Network with offline training
is a good for disease prediction in early stage and the good
performance of the system can be obtained by preprocessed and
normalized dataset. The classification accuracy can be improved
by reduction in features.
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