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ISSN (ONLINE):2395-695X
ISSN (PRINT):2395-695X
International Journal of Advanced Research in Basic Engineering Sciences and Technology (IJARBEST)
Vol.3, Issue.3, March 2017
Early Prediction of Heart Disease Using
Decision Tree Algorithm
A. Sankari Karthiga1, M. Safish Mary2, M. Yogasini3
M.Phil. Scholar1, Assistant Professor2, Assistant Professor3,
Mother Teresa Women’s University, Kodaikanal1
St. Xavier’s College, Tirunelveli2
Sadakathdulla Appa College, Tirunelveli3
sankarikarthiga.sk@gmail.com
Abstract-For processing of large amount of data numerous techniques are used. Data
Mining is one of the techniques that are used most often. To process these data, Data
mining combines traditional data analysis with sophisticated algorithms. Medical data
mining is an important area of Data Mining and considered as one of the important
research field due to its application in healthcare domain. Classification and prediction
of medical datasets poses challenges in Medical Data Mining. The heart disease
accounts to be the leading cause of death worldwide. It is difficult for medical
practitioners to predict the heart attack as it is a complex task that requires experience
and knowledge. The health sector today contains hidden information that can be
important in making decisions. Data mining algorithms such as decision tree and Naïve
Bayes are applied in this research for predicting heart attacks. The research result
shows prediction accuracy of 99%. Data mining enable the health sector to predict
patterns in the dataset.
Index Terms- Decision Tree Algorithm, Naïve Bayes Algorithm.
I. INTRODUCTION
1.1. DATA MINING
Data Mining is about explaining the past and predicting the future by means of data
analysis. Data mining is a multi-disciplinary field that combines statistics, machine learning,
artificial intelligence and database technology. The value of data mining applications is often
estimated to be very high. Many businesses have stored large amounts of data over years of
operation, and data mining is able to extract very valuable knowledge from this data. The
businesses are then able to leverage the extracted knowledge into more clients, more sales,
and greater profits. This is also true in the engineering and medical fields.
1.1.1. Statistics
The science of statistics is to collecting, classifying, summarizing, organizing, analyzing, and
interpreting data.
1.1.2. Artificial Intelligence
The study of computer algorithms is to dealing with the simulation of intelligent behaviour in
order to perform those activities that are normally thought to require intelligence.
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1.1.3. Machine Learning
The study of the computer algorithms aim is to learn in order to improve automatically
through experience.
1.1.4. Database
The science and technology of collecting, storing and managing data so users can retrieve,
add, update or remove such data.
1.1.5. Data warehousing
The science and technology of collecting, storing and managing data with advanced multi-
dimensional reporting services in support of the decision-making processes.
1.1.6. Explaining the Past
Data mining explains the past through data exploration.
1.1.7. Predicting the Future
Data mining predicts the future by means of modeling.
1.1.8. Data Exploration
Data Exploration is about describing the data by means of statistical and visualization
techniques. We explore data in order to bring important aspects of that data into focus for
further analysis.
“Data Mining is a non-trivial extraction of implicit, previously unknown and potential
useful information about data” [1]. In short, it is a process of analyzing data from different
perspective and gathering the knowledge from it. The discovered knowledge can be used for
different applications for example healthcare industry. Nowadays healthcare industry
generates large amount of data about patients, disease diagnosis etc. Data mining provides a
set of techniques to discover hidden patterns from data. A major challenge facing Healthcare
industry is quality of service. Quality of service implies diagnosing disease correctly &
provides effective treatments to patients. Poor diagnosis can lead to disastrous consequences
that are unacceptable.
According to survey of WHO, 17 million total global deaths are due to heart attacks
and strokes. The deaths due to heart disease in many countries occur due to work overload,
mental stress and many other problems. Overall, it is found as primary reason behind death in
adults. Diagnosis is complicated and important task that needs to be executed accurately and
efficiently. The diagnosis is often made, based on doctor’s experience & knowledge. This
leads to unwanted results & excessive medical costs of treatments provided to patients.
Therefore, an automatic medical diagnosis system is designed that take advantage of
collected database and decision support system. This system can help in diagnosing disease
with less medical tests & effective treatments.
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1.2. MEDICAL DATA MINING
Medical data mining has great potential for exploring the hidden patterns in the data
sets of the medical domain. These patterns can be utilized for clinical diagnosis. However,
the available raw medical data are widely distributed, heterogeneous in nature, and
voluminous. These data need to be collected in an organized form. This collected data can be
then integrated to form a hospital information system. Data mining technology provides a
user oriented approach to novel and hidden patterns in the data.
The World Health Organization has estimated that 12 million deaths occurs
worldwide, every year due to the Heart diseases. Half the deaths in the United States and
other developed countries occur due to cardio vascular diseases. It is also the chief reason of
deaths in numerous developing countries. On the whole, it is regarded as the primary reason
behind deaths in adults. The term Heart disease encompasses the diverse diseases that affect
the heart. Heart disease was the major cause of casualties in the different countries including
India. Heart disease kills one person every 34 seconds in the United States. Coronary heart
disease, Cardiomyopathy and Cardiovascular disease are some categories of heart diseases.
The term “cardiovascular disease” includes a wide range of conditions that affect the heart
and the blood vessels and the manner in which blood is pumped and circulated through the
body. Cardiovascular disease (CVD) results in several illness, disability, and death. The
diagnosis of diseases is a vital and intricate job in medicine.
Medical diagnosis is regarded as an important yet complicated task that needs to be
executed accurately and efficiently. The automation of this system would be extremely
advantageous. Regrettably all doctors do not possess expertise in every sub specialty and
moreover there is a shortage of resource persons at certain places. Therefore, an automatic
medical diagnosis system would probably be exceedingly beneficial by bringing all of them
together. Appropriate computer-based information and/or decision support systems can aid in
achieving clinical tests at a reduced cost. Efficient and accurate implementation of automated
system needs a comparative study of various techniques available. This paper aims to analyze
the different predictive/ descriptive data mining techniques proposed in recent years for the
diagnosis of heart disease.
Medical diagnosis is considered as a significant yet intricate task that needs to be
carried out precisely and efficiently. The automation of the same would be highly beneficial.
Clinical decisions are often made based on doctor’s intuition and experience rather than on
the knowledge rich data hidden in the database. This practice leads to unwanted biases, errors
and excessive medical costs which affects the quality of service provided to patients. Data
mining have the potential to generate a knowledge-rich environment which can help to
significantly improve the quality of clinical decisions.
Decision Tree is a popular classifier which is simple and easy to implement. It
requires no domain knowledge or parameter setting and can handle high dimensional data.
The results obtained from Decision Trees are easier to read and interpret. The drill through
feature to access detailed patients‟ profiles is only available in Decision Trees.
Naïve Bayes is a statistical classifier which assumes no dependency between
attributes. It attempts to maximize the posterior probability in determining the class. The
advantage of using naive bayes is that one can work with the naive Bayes model without
using any Bayesian methods. Naive Bayes classifiers have works well in many complex real-
world situations
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1.3. HEART DISEASE
The heart is important organ of human body part. It is nothing more than a pump,
which pumps blood through the body. If circulation of blood in body is inefficient the organs
like brain suffer and if heart stops working altogether, death occurs within minutes. Life is
completely dependent on efficient working of the heart. The term Heart disease refers to
disease of heart & blood vessel system within it.
A number of factors have been shown that increases the risk of Heart disease:
• Family history
• Smoking
• Poor diet
• High blood pressure
• High blood cholesterol
• Obesity
• Physical inactivity
• Hyper tension
Factors like these are used to analyze the Heart disease. In many cases, diagnosis is
generally based on patient’s current test results & doctor’s experience. Thus the diagnosis is a
complex task that requires much experience & high skill.
Heart disease is a broad term that includes all types of diseases affecting different
components of the heart. Heart means 'cardio.' Therefore, all heart diseases belong to the
category of cardiovascular diseases. Some types of Heart diseases are
1. Coronary heart disease It also known as coronary artery disease (CAD), it is
the most common type of heart disease across the world. It is a condition in
which plaque deposits block the coronary blood vessels leading to a reduced
supply of blood and oxygen to the heart.
2. Angina pectoris it is a medical term for chest pain that occurs due to
insufficient supply of blood to the heart. Also known as angina, it is a warning
signal for heart attack. The chest pain is at intervals ranging for few seconds or
minutes.
3. Congestive heart failure it is a condition where the heart cannot pump enough
blood to the rest of the body. It is commonly known as heart failure.
4. Cardiomyopathy, it is the weakening of the heart muscle or a change in the
structure of the muscle due to inadequate heart pumping. Some of the common
causes of cardiomyopathy are hypertension, alcohol consumption, viral
infections, and genetic defects.
5. Congenital heart disease, it also known as congenital heart defect, it refers to
the formation of an abnormal heart due to a defect in the structure of the heart
or its functioning. It is also a type of congenital disease that children are born
with.
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6. Arrhythmias it is associated with a disorder in the rhythmic movement of the
heartbeat. The heartbeat can be slow, fast, or irregular. These abnormal
heartbeats are caused by a short circuit in the heart's electrical system.
7. Myocarditis it is an inflammation of the heart muscle usually caused by viral,
fungal, and bacterial infections affecting the heart. It is an uncommon disease
with few symptoms like joins pain, leg swelling or fever that cannot be
directly related to the heart.
1.4. DECISION TREES
The decision tree approach is more powerful for classification problems. There are
two steps in this techniques building a tree & applying the tree to the dataset. There are many
popular decision tree algorithms CART, ID3, C4.5, CHAID, and J48. From these J48
algorithm is used for this system. J48 algorithm uses pruning method to build a tree. Pruning
is a technique that reduces size of tree by removing over fitting data, which leads to poor
accuracy in predications. The J48 algorithm recursively classifies data until it has been
categorized as perfectly as possible. This technique gives maximum accuracy on training
data. The overall concept is to build a tree that provides balance of flexibility & accuracy.
1.5. NAIVE BAYES
Naive Bayes classifier is based on Bayes theorem. This classifier algorithm uses
conditional independence, means it assumes that an attribute value on a given class is
independent of the values of other attributes.
1.6. ORGANIZATION OF THE THESIS
This chapter is organized as follows: first, we outline the basics of patient physiology
and fetus response to different stages of oxygen deficiency - hypo anemia, hypoxia, and
asphyxia. Next, we describe an interaction between mother and fetus during gestation with
emphasis on the antepartum and intrapartum period. Finally, we introduce methods for the
patient hypoxia diagnostics with focus on electronic patient monitoring that involves
observation of CTG or FECG changes. We stress the significance of signal interpretation and
describe advantages and disadvantages of respective methods.
II. CLASSIFICATION USING DECISION TREE ALGORITHM
2.1. INTRODUCTION
Decision tree builds classification or regression models in the form of a tree structure.
It breaks down a dataset into smaller and smaller subsets while at the same time an associated
decision tree is incrementally developed. The final result is a tree with decision nodes and
leaf nodes. A decision node (e.g., Outlook) has two or more branches (e.g., Sunny, Overcast
and Rainy). Leaf node (e.g., Play) represents a classification or decision. The topmost
decision node in a tree which corresponds to the best predictor called root node. Decision
trees can handle both categorical and numerical data.
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2.2. ALGORITHM
The core algorithm for building decision trees called C4.5 by J. R. Quinlan which
employs a top-down, greedy search through the space of possible branches with no
backtracking. C4.5 uses Entropy and Information Gain to construct a decision tree.
2.3. ENTROPY
A decision tree is built top-down from a root node and involves partitioning the data
into subsets that contain instances with similar values (homogenous). ID3 algorithm uses
entropy to calculate the homogeneity of a sample. If the sample is completely homogeneous
the entropy is zero and if the sample is an equally divided it has entropy of one. To build a
decision tree, we need to calculate two types of entropy using frequency tables as follows:
a) Entropy using the frequency table of one attributes:
b) Entropy using the frequency table of two attributes:
2.4. INFORMATION GAIN
The information gain is based on the decrease in entropy after a dataset is split on an
attribute. Constructing a decision tree is all about finding attribute that returns the highest
information gain (i.e., the most homogeneous branches).
Step 1: Calculate entropy of the target.
Step 2: The dataset is then split on the different attributes. The entropy for each branch is
calculated. Then it is added proportionally, to get total entropy for the split. The resulting
entropy is subtracted from the entropy before the split. The result is the Information Gain, or
decrease in entropy.
Step 3: Choose attribute with the largest information gain as the decision node.
Step 4(a): A branch with entropy of 0 is a leaf node.
Step 4(b): A branch with entropy more than 0 needs further splitting.
Step 5: The ID3 algorithm is run recursively on the non-leaf branches, until all data is
classified.
2.5. DECISION TREE TO DECISION RULES
A decision tree can easily be transformed to a set of rules by mapping from the root
node to the leaf nodes one by one.
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III. CLASSIFICATION USING NAIVE BAYES CLASSIFIER
A. INTRODUCTION
The Naive Bayesian classifier is based on Bayes’ theorem with independence
assumptions between predictors. A Naive Bayesian model is easy to build, with no
complicated iterative parameter estimation which makes it particularly useful for very large
datasets. Despite its simplicity, the Naive Bayesian classifier often does surprisingly well and
is widely used because it often outperforms more sophisticated classification methods.
B. ALGORITHM
Bayes theorem provides a way of calculating the posterior probability, P (c|x), from P(c),
P(x), and P (x|c). Naive Bayes classifier assumes that the effect of the value of a predictor (x)
on a given class (c) is independent of the values of other predictors. This assumption is called
class conditional independence.
P (c|x) is the posterior probability of class (target) given predictor (attribute).
P(c) is the prior probability of class.
P (x|c) is the likelihood which is the probability of predictor given class.
P(x) is the prior probability of predictor.
Thus, we can write:
Since there is a total of 60 objects, 40 of which are GREEN and 20 RED, our prior
probabilities for class membership are:
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Having formulated our prior probability, we are now ready to classify a new object
(WHITE circle). Since the objects are well clustered, it is reasonable to assume that the more
GREEN (or RED) objects in the vicinity of X, the more likely that the new cases belong to
that particular color. To measure this likelihood, we draw a circle around X which
encompasses a number (to be chosen a priori) of points irrespective of their class labels. Then
we calculate the number of points in the circle belonging to each class label. From this we
calculate the likelihood:
From the illustration above, it is clear that Likelihood of X given GREEN is smaller than
Likelihood of X given RED, since the circle encompasses 1 GREEN object and 3 RED ones.
Thus:
Although the prior probabilities indicate that X may belong to GREEN (given that there are
twice as many GREEN compared to RED) the likelihood indicates otherwise; that the class
membership of X is RED (given that there are more RED objects in the vicinity of X than
GREEN). In the Bayesian analysis, the final classification is produced by combining both
sources of information, i.e., the prior and the likelihood, to form a posterior probability using
the so-called Bayes' rule (named after Rev. Thomas Bayes 1702-1761).
Finally, we classify X as RED since its class membership achieves the largest posterior
probability.
Naive Bayes can be modelled in several different ways including normal, lognormal, gamma
and Poisson density functions:
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C. PERFORMANCE ANALYSES
(i) DECISION TREE CLASSIFIER-CROSS VALIDATION (EXPERIMENTAL RESULTS)
(ii) DECISION TREE PERFORMANCE METRICS
METHOD
DECISION TREE
Accuracy
98.2753
Sensitivity
95.452
Specificity
97.7919
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(iii) SUMMARY
The constructing decision tree techniques are generally computationally
inexpensive, making it possible to quickly construct models even when the training set size is
very large. Furthermore, once a decision tree has been built, classifying a test record is
extremely fast.
D. NAÏVE BAYES
(i) EXPRIMENTAL RESULTS
(ii) NAÏVE BAYES PERFORMANCE METRICS
METHOD
NAIVE BAYES
Accuracy
89.9028
Sensitivity
70.9042
Specificity
85.5353
98.2753
95.452
97.7919
94
94.5
95
95.5
96
96.5
97
97.5
98
98.5
Accuracy Sensitivity Specificity
DECISION TREE
DECISION TREE
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(iii) SUMMARY
Poisson variables are regarded here as continuous since they are ordinal rather than truly
categorical. For categorical variables, a discrete probability is used with values of the
categorical level being proportional to their conditional frequency in the training data.
IV. RESULT ANALYSIS
The dataset consists of total 573 records in Heart disease 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. The data mining tool MATLAB is used for experiment.
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 from MATLAB. The ReplaceMissingValues filter scans all records &
replaces missing values with mean mode method. This process is known as Data Pre-
processing. After pre-processing the data, data mining classification techniques such as
Neural Networks, Decision Trees, & Naive Bayes were applied.
A confusion matrix is obtained to calculate the accuracy of classification. A confusion
matrix shows how many instances have been assigned to each class. In our experiment we
have two classes, and therefore we have a 2x2 confusion matrix.
Class a = YES (has heart disease)
Class b = NO (no heart disease)
89.9028
70.9042
85.5353
0
20
40
60
80
100
Accuracy Sensitivity Specificity
NAIVE BAYES
NAIVE BAYES
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V. CONFUSION MATRIX
TP (True Positive): It denotes the number of records classified as true while they were
actually true.
FN (False Negative): It denotes the number of records classified as false while they were
actually true.
FP (False Positive): It denotes the number of records classified as true while they were
actually false.
TN (True Negative): It denotes the number of records classified as false while they were
actually false.
Confusion matrix obtained for three classification methods with 13 attributes
CONFUSION MATRIX FOR NAIVE BAYES
CONFUSION MATRIX FOR DECISION TREES
The classification task is to generalize well on unseen/independent data. A classifier is
learned on training/learning data and then tested on data that has not been used for learning
(unseen test data). There exist many measures to assess performance of a classifier and a lot
of techniques to create training and test data in order to estimate generalization ability of a
classifier on test (unseen) data.
Heart disease dataset: UCI Machine Learning Repository.
CHARACTERISTICS OF A DATA SET
Data Set Characteristics
Multivariate
Attribute Characteristics
Real
Associated tasks
Classification
Number of Instances
573
Number of Attributes
13
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CLASS INFORMATION:
The PHR pattern classification for the three class are.
– Category I (Normal)
– Category II (Disease)
VI. PERFORMANCE EVALUATION
This is a measurement tool to calculate the performance:
• Accuracy =
• Sensitivity =
• Specificity =
PERFORMANCE METRICS OF DT AND NB
METHOD
DECISION TREE
NAIVE BAYES
Accuracy
98.2753
89.9028
Sensitivity
95.452
70.9042
Specificity
97.7919
85.5353
98.2753
89.9028
84
86
88
90
92
94
96
98
100
DECISION
TREE NAIVE
BAYES
ACCURACY
Accuracy
0
20
40
60
80
100
120
DECISION
TREE NAIVE
BAYES
SENSITIVITY
Sensitivity
FNTPTP
FNFPTNTP TNTP
FPTNTN
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SUMMARY OF THE CLASSIFICATION ACCURACY – DECISION TREE CLASSIFIER - CROSS
VALIDATION
NORMAL
DISEASE
Accuracy
97.6482
99.3885
Sensitivity
98.3686
93.7500
Specificity
95.1168
99.8974
SUMMARY OF THE CLASSIFICATION ACCURACY – NAIVE BAYES CLASSIFIER - CROSS
VALIDATION
NORMAL
DISEASE
Accuracy
87.3001
93.3208
Sensitivity
93.7160
82.3864
Specificity
64.7558
94.3077
97.7919
85.5353
75
80
85
90
95
100
DECISION
TREE NAIVE
BAYES
SPECIFICITY
Specificity
90
92
94
96
98
100
102
Percentage of Accuracy
Diagnosis Result
DECISION TREE CLASSIFICATION
USING CROSS VALIDATION
NORMAL
DISEASE
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PERFORMANCE ANALYSIS FOR FOUR CLASSIFIERS - 10 FOLD CROSS VALIDATION
METHOD
DECISION TREE
NAIVE BAYES
Accuracy
98.2753
89.9028
Sensitivity
95.4520
70.9042
Specificity
97.7919
85.5353
VII. CONCLUSION
The overall objective of our work is to predict more accurately the presence of heart
disease. In this paper, UCI repository dataset are used to get more accurate results. Three data
mining classification techniques were applied namely Decision trees and Naive Bayes. From
results, it has been seen that Decision trees provides accurate results as compare to Naive
Bayes. This system can be further expanded. It can use more number of inputs. Other data
mining techniques can also be used for predication e.g. Clustering, Time series, Association
rules. The text mining can be used to mine huge amount of unstructured data available in
healthcare industry database.
0
20
40
60
80
100
Accuracy Sensitivity Specificity
Percentage of Accuracy
Diagnosis Result
NAIVE BAYES CLASSIFICATION USING
CROSS VALIDATION
NORMAL
DISEASE
0
20
40
60
80
100
120
Percentage
Performance
PERFORMANCE METRICS
COMPARISION OF SENSITIVITY FOR
TWO CLASSIFIERS
DECISION TREE
NAIVE BAYES
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[11] Antepartum patient heart rate feature extraction and classification using empirical mode decomposition
and support vector machine, Niranjana KrupaEmail author, Mohd Ali MA, Edmond Zahedi, Shuhaila
Ahmed and Fauziah M Hassan, January 2011
[12] Performance Evaluation of K-Means and Heirarichal Clustering in Terms of Accuracy and Running
Time, Nidhi Singh,.Divakar Singh, Department of computer Science &
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[13] Determination of Patient State from Cardiotocogram Using LS-SVM with Particle Swarm
Optimization and Binary Decision Tree, Ersen Yılmaz and Çağlar Kılıkçıer Electrical-Electronic
Engineering Department, Uludag University, 16059 Gorukle, Bursa, Turkey, Received 26 June 2013;
Accepted 6 September 2013.
A. Sankari Karthiga, M. Safish Mary, M. Yogasini ©IJARBEST PUBLICATIONS
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