Prediction of Heart Disease Using Classification Based Data Mining Techniques

Abstract

Data Mining is an interesting field of research whose major objective is to find interesting and useful patterns from huge data sets. These patterns can be further used to make important decisions based on the result of the analysis. Healthcare industry today generates huge amount of data on a day to day basis. This data has to be analysed and hidden and meaningful patterns can be discovered. Data mining plays a promising and significant role in this aspect. Data Mining techniques can be used for disease prediction. In this research, the classification based data mining techniques are applied to healthcare data. This research focuses on the prediction of heart disease using three classification techniques namely Decision Trees, Naïve Bayes and K Nearest Neighbour.

Full text

Prediction of Heart Disease Using
Classification Based Data Mining
Techniques
Sujata Joshi and Mydhili K. Nair
Abstract Data Mining is an interesting field of research whose major objective is
to find interesting and useful patterns from huge data sets. These patterns can be
further used to make important decisions based on the result of the analysis.
Healthcare industry today generates huge amount of data on a day to day basis. This
data has to be analysed and hidden and meaningful patterns can be discovered. Data
mining plays a promising and significant role in this aspect. Data Mining techniques
can be used for disease prediction. In this research, the classification based data
mining techniques are applied to healthcare data. This research focuses on the
prediction of heart disease using three classification techniques namely Decision
Trees, Naïve Bayes and K Nearest Neighbour.
Keywords Data mining Classification technique Heart disease Healthcare 
Decision tree Naïve bayes K-Nearest neighbor Dataset
1 Introduction
Heart Disease is a class of diseases that involve the heart, the blood vessels or both.
The most common causes of heart disease are atherosclerosis and/or hypertension.
Atherosclerosis is a condition that develops when a substance called plaque builds
up in the walls of the arteries. This buildup narrows the arteries, making it harder
for blood to flow through. If a blood clot forms, it can stop the blood flow. This can
S. Joshi (&)
Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology,
Bangalore, Karnataka, India
e-mail: sujata_msrp@yahoo.com
M.K. Nair
Department of Information Science and Engineering, M. S. Ramaiah Institute of Technology,
Bangalore, Karnataka, India
e-mail: mydhili.nair@gmail.com
©Springer India 2015
L.C. Jain et al. (eds.), Computational Intelligence in Data Mining - Volume 2,
Smart Innovation, Systems and Technologies 32, DOI 10.1007/978-81-322-2208-8_46
503

cause a heart attack or stroke. The major risk factors for heart diseases are age,
gender, high blood pressure, diabetes mellitus, tobacco smoking, processed meat
consumption, excessive alcohol consumption, sugar consumption, family history,
obesity, lack of physical activity, psychosocial factors, and air pollution.
Heart disease is the leading cause of deaths worldwide, however since the 1970s,
mortality rate due to heart related diseases have declined in many high-income
countries. At the same time, heart related deaths and diseases have increased at a
fast rate in low and middle-income countries. Although heart disease usually affects
older adults, the symptoms may begin in early life, making primary prevention
efforts necessary from childhood. Therefore risk factors may be modified by having
healthy eating habits, exercising regularly, and avoiding of smoking tobacco.
In today’s world, most of the hospitals maintain their patient data in electronic
form through some hospital database management system. These systems generate
huge amount of data on a daily basis. This data may be in the form of free text,
structured as in databases or in the form of images. This data may be used to extract
useful information which may be used for decision making. This requirement has
led to the use of Knowledge Discovery in Databases (KDD) which is responsible
for transforming data of low-level into high-level knowledge for decision making.
Data mining which is one of the KDD process aims at finding useful patterns from
large datasets. These patterns can be further analyzed and the result can be used for
effective decision making and analysis. The various tasks of data mining are
classification, clustering, association analysis and outlier detection. In this paper,
various data mining classification techniques are applied to healthcare data related
to heart diseases. It has helped to determine the best prediction technique in terms of
its accuracy and error rate on the specific dataset.
2 Related Work
There has been an increase in the number of people suffering from heart diseases in
the recent years [1]. With the advent of information technology and its applications
data mining plays a very important and apt role in early detection of diseases. Data
mining is extensively used in all fields and healthcare industry in particular [2–6]. In
the healthcare industry, the data mining techniques are used for diagnosis of dis-
eases [7], disease prediction [8], and analysis [9]. Data mining techniques can be
applied for predicting the outcome of interest. Hence prediction is a very important
task. The issues and guidelines of Predictive data mining in clinical medicine is
discussed in [10]. Research work [7,11,12] related to heart disease diagnosis using
data mining techniques is the motivation for this work. Classification based on Gini
index is discussed in [13]. The data mining techniques Decision tree, Naïve Bayes
and KNN are discussed in [8,10,14,15]. A model based on Combination of Naïve
Bayes Classifier and K-Nearest Neighbor is proposed in [16]. A clinical decision
support system using association rule mining is discussed in [17]. A prediction
system for lung cancer detection is proposed in [18]. A diagnostic tool is proposed
504 S. Joshi and M.K. Nair

in [19] for skin diseases. In [6,9], the researchers analyze healthcare data using
different data mining techniques. After the extensive literature survey of the dataset,
algorithms, methods employed by the authors, results and future work, it is found
that there is a lot of scope in discovering efficient methods of medical diagnosis for
various diseases and their analysis. This work is an attempt to predict the occur-
rence of heart diseases using classification data mining techniques namely Decision
Tree, Naïve Bayes and K-Nearest Neighbor techniques.
3 Classification
Classification is one of the important data mining tasks. The objective of classifi-
cation is to assign a class to previously unseen data accurately. Classification
consists of two stages:
Stage 1: Model construction
Stage 2: Model usage
Classification creates a model for the attributes of the dataset. A dataset is
divided into training set and test set. In the first stage the training set is used to build
the classification model using a learning algorithm. In the second stage, the learned
model is put into operational use i.e. it is used to validate the test set. If the model
performs well, then the model is now ready for prediction.
3.1 Classification Techniques
In this study, the classification techniques, Decision tree, Naïve Bayes and KNN are
explored and applied to the dataset.
3.1.1 Decision Trees
The decision tree is a structure that includes root node, branch and leaf node. Each
internal node denotes a test on an attribute, each branch denotes the outcome of test
and each leaf node holds the class label. The first node in the tree is the root node.
First, an attribute is selected and placed at the root node, and a branch is made for
each possible value. This splits up the data set into subsets, one for every value of
the attribute. Now repeat the process recursively for each branch, using only those
instances that actually reach the branch. When all instances at a node have the same
classification, the tree development can be stopped. To select the best split the
measures used generally are Gini, Entropy or Classification error.
Prediction of Heart Disease …505

3.1.2 Naïve Bayes Classifier
Classification based on Bayes Theory is known as Bayesian Classification. Naive
Bayes classifier is a statistical based classifier which is based on Bayes Theory. It
assumes that attributes are statistically independent. This classifier is based on
probabilities.
Given two events A and B, P(A) is prior probability and P(A|B) is posterior
probability, then according to Bayes theorem
PðAjBÞ¼P(B/A)P(A)/P(B) and P(BjA) is computed as P(A \B)=P(A)
These Bayesian probabilities are used to determine the most likely next event for
the given instance given all the training data. Conditional probabilities are deter-
mined from the training data.
This classifier yields optimal prediction (given the assumptions). It can also
handle discrete or numeric attribute values.
3.1.3 K-Nearest Neighbor
Nearest neighbor method is a instance based classification technique that remembers
all the instances. When the new instance is encountered, it uses previous instances as
a model and compares it with the new instance. Prediction for the current instance is
the one with the most similar previously observed instance. K-NN classifies the
instances using the K nearest neighbors. This classifier has faster training rate but is
slow when the dataset is large since it has to evaluate all instances.
4 Methodology
4.1 TOOL Used
WEKA [20] Tool (Waikato Environment for Knowledge Analysis), is a set of data
mining algorithms and tools which can be used for analysis of data. WEKA is
developed in JAVA. WEKA allows analyzing the data sets saved in .arff format using
various algorithms. In this study, the Decision tree, Naïve Bayes and K-NN algor ithms
are applied to heart data set and the results of applying these techniques are shown.
4.2 Data Source
The heart diseases data set from the UCI [21] Learning Repository is used for this
study. The heart data set consists of 303 records and 14 attributes. The attributes are
listed in Table 1.
506 S. Joshi and M.K. Nair

4.3 Decision Tree
The decision tree is created by selecting the best split at every node. To select the
best attribute for the split, the information gain is computed at each node and the
attributes are ranked accordingly. Here the attribute evaluator used is Gain Ratio
AttributeEval and the search method used is Ranker method from WEKA Tool. The
ranked attributes are listed in Table 2.
Table 1 Attributes of the heart.arff file
No Attribute Type
1 age Real
2 sex {female, male}
3 cp {typ_angina, asympt, non_anginal, atyp_angina}
4 trestbps Real
5 chol Real
6 restecg {left_vent_hyper, normal, st_t_wave_abnormality}
7 thalach real
8 restecg {left_vent_hyper, normal, st_t_wave_abnormality}
9 exang {no, yes}
10 oldpeak real
11 slope {up, flat, down}
12 ca real
13 thal {fixed_defect, normal, reversable_defect}
14 num {‘<50’,‘>50_1’,’>50_2’,‘>50_3’,‘>50_4’}
Table 2 Attribute ranking
based on information gain Info gain Rank Attribute
0.17 12 thal
0.16 13 ca
0.15 9 exang
0.13 8 thalach
0.11 3 cp
0.10 10 oldpeak
0.09 11 slope
0.065 2 sex
0.060 1 age
0.022 7 restecg
0 6 fbs
0 5 chol
0 4 trestbps
Prediction of Heart Disease …507

The attributes selected in the order are: 12,13,9,8,3,10,11,2,1,7,6,5, 4.
The Decision Tree algorithm J48 is then applied to the heart data set and the
decision tree in Fig. 1is generated. This decision tree can be used for prediction.
The results are shown in Table 3.
4.4 Naïve Bayes
The attribute evaluator used is Gain Ratio AttributeEval and the search method used
is Ranker method. The ranked attributes are same as in Decision tree. The Naïve
Bayes algorithm is applied to the heart data set and the results of few attributes are
shown in Table 4.
The results are shown in Table 5.
Fig. 1 Decision tree generated using J48 algorithm
Table 3 Results of decision tree algorithm
No of instances Percentage (%)
Correctly classified instances 279 92.0792
Incorrectly classified instances 24 7.9208
Total instances 303
508 S. Joshi and M.K. Nair

4.5 K-Nearest Neighbor
The KNN algorithm is applied to the heart data set and the results are shown in
Table 6.
5 Results and Conclusion
The evaluation measures used are Sensitivity, Specificity and Accuracy
(i) Sensitivity =TP/P
(ii) Specificity =TN/N
(iii) Accuracy =(TP +TN)/(P+N)
Table 4 Results of few attributes using Naïve Bayes technique
Attribute <50 (0.54) >50_1 (0.45) >50_2 (0) >50_3 (0) >50_4 (0)
cp
typ_angina 17.0 8.0 1.0 1.0 1.0
asymp 40.0 105.0 1.0 1.0 1.0
non_anginal 70.0 19.0 1.0 1.0 1.0
atyp_angina 42.0 10.0 1.0 1.0 1.0
[total] 169.0 142.0 4.0 4.0 4.0
restecg
left_vent_hyper 69.0 80.0 1.0 1.0 1.0
Normal 97.0 57.0 1.0 1.0 1.0
st_t_wave_abnormality 2.0 4.0 1.0 1.0 1.0
[total] 168.0 141.0 3.0 3.0 3.0
Table 5 Results of Naïve Bayes technique
No of instances Percentage (%)
Correctly classified instances 255 84.1584
Incorrectly classified instances 48 15.8416
Total instances 303
Table 6 Results of K-nearest neighbor technique
No of instances Percentage (%)
Correctly classified instances 303 100
Incorrectly classified instances 0 0
Total instances 303
Prediction of Heart Disease …509

where TP is true positives, TN is true negatives, P and T are actual positives and
actual negatives respectively. A good predictor must have high sensitivity, low
specificity and high accuracy. The comparisons of these measures with respect to
the three prediction techniques are summarized in Table 7.
The experiments are conducted with WEKA tool and the algorithms applied on
the heart dataset. The graph in Fig. 2reveals that sensitivity and accuracy are high
and specificity is low. Hence the predictors perform well on operational use. With
respect to model creation the results show that KNN has highest accuracy as
expected since KNN remembers all the instances. But when used for prediction the
Decision Tree performs well when compared to other two methods for the given
heart dataset.
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KNN 1 0 1
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