Content uploaded by Muhammad Zeeshan Younas
Author content
All content in this area was uploaded by Muhammad Zeeshan Younas on Nov 26, 2021
Content may be subject to copyright.
INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 08 ISSUE: 04 | APR 2021 WWW.IRJET.NET P-ISSN: 2395-0072
© 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3539
Effective Heart Disease Prediction using Machine Learning and Data
Mining Techniques
Muhammad Zeeshan Younas
Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan
-------------------------------------------------------------------------***----------------------------------------------------------------------
Abstract-- Nowadays, heart disease is one of the prevailing main
causes of morbidity and mortality. It is a hot health topic in our daily
life, and heart disease treatment is very complicated. It is one-third
of all deaths globally, stroke and heart disease. They both are
globally the biggest killer, and their diagnosis availability is
infrequent, especially in developing countries. This paper contains a
framework based on some machine learning and data mining
classification techniques on the heart disease dataset. There is no
operational use of the data produced from the hospitals. Some
convinced tools are used to extract the facts from the database to
recognize the heart. This work is done by using Cleveland heart
disease dataset that is sourced from the "UCI Machine Learning (ML)
repository" to test and analyze on some various supervised ML and
data mining techniques, some different attributes associated with
causing of cardiovascular heart disease age, sex, chest pain type,
chol, thal, etc. We will use these respective data to a model that will
predict whether the patient has heart disease or not. This paper
discussed the results of the modern techniques and will be used to
predict the results for heart disease by summarizing some current
research. The proposed method works best result in 86.89%
accuracy by using a logistic regression algorithm.
Keywords- Machine Learning, Classification Techniques,
Prediction, Data Mining, Heart Disease, Python Programming.
I. INTRODUCTION
Data mining is a process that is used for mining information
or knowledge from a huge database. It is an essential and
significant step for discovering knowledge from existing
databases. Data mining's primary task is that extract the
hidden information and knowledge from the vast database. It
is identified as Knowledge Discovery in Database (KDD). It is
an important process where some common data mining
techniques are used to extract the data arrangement. Data
mining's technique helps to organizations to gain knowledge-
based information. It includes understanding the business,
data preparation, evaluating the data, and deployment. Its
techniques work very rapidly and can find large amounts of
data with the short passage of time. More likely, sometimes, it
is referred to as knowledge discovery in databases. Suppose
we use some professional and proficient computerized
systems that are based on data mining and machine-learning
algorithms. In that case, they can help us for achieving clinical
assessments or diagnoses to minimize heart disease risk.
Machine learning is self-restraint that deals with
programming, and it learns automatically and improves with
experience. Bayesian and data mining analysis is trending,
adding the demand for machine learning. Data mining has
four different main techniques: cluster, Regression,
Classification, and association rules. Classification is a
fundamental technique in data mining. We can get the future
outcome and predict the data based on historical data
available in a database. The dataset can be classified into two
categories through the classification technique, namely Yes
and No. This method can achieve relevant and essential
information for data and easily classify our data into different
classes. "Data mining is the method for determining
potentially useful arrangements through huge data sets and a
large amount of database or metadata. It comes from
different data sources, it may be sorted in various data
warehouses and data mining sorting techniques" [2].
Knowledge Discovery in Database (KDD) is used for data
integration and cleaning, data discovery patterns, Knowledge
Presentation, data selection, and data transformation.
Healthcare association produces broad data to mark the
factual decisions.
Figure 1. Process of knowledge discovery in data.
Data mining whole process is based on some various steps for
Extracting respective knowledge. Data cleaning in data
mining is how we can remove noise and corrupt or inaccurate
records from data. We can prepare correct and complete data
for data analysis by eliminating duplication in data through
the data cleaning process. This data is usually not helpful
when it comes to data analysis. The data cleaning process
helps ensure that respective information is matched with the
field and ensures data selection and transformation. The data
transformation process is used to transform the data in a
INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 08 ISSUE: 04 | APR 2021 WWW.IRJET.NET P-ISSN: 2395-0072
© 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3540
proper way required by data mining procedures. The pattern
evaluation is used to represents knowledge based on
different measures of interest that are given. We can use
other heart disease patients' data collected after some
diagnosis analysis and utilize the experience and knowledge
of several specialists split with the same symptoms of
coronary heart diseases. Complete and correct data helps the
diagnosis analysis of patients for providing efficient
treatment.
This paper aims to identify and categorize some important
feature classification using data mining and ML techniques to
predict cardiovascular heart disease and supervised ML
algorithms. The regression and classification model is the
main model that is used in supervised machine learning, and
this research work is based on some classifications models.
This methodology predicts and compares the following two
main machine learning classification and data mining
classification algorithms, logistic regression, and naive Bayes
classification to compare and confuse matrix. Cleveland
dataset is selected, and this dataset is gathered from the UCI
ML repository. These models are performed by using Python
Programming Language. This paper discussed the results of
the current technique and predicted the result for heart
disease. Additionally, the experiment results compare the
accuracy achieved by these algorithms and evaluated results
by various respective authors.
II. HEART DISEASE: AN OVERVIEW
World Health Organization (WHO, 2017) every year, heart
disease is becoming the cause of approximately 17.7 million
deaths worldwide. It is one-third of all deaths in the whole
world. Stroke and heart disease both are the global biggest
killers. Suppose we enable these techniques for the medical
diagnosis center. In that case, it will be more beneficial and
minimize the overall cost after associating various data
mining techniques for showing their appropriateness results
[3]. Coronary Artery Disease (CAD) is the primary cause and
widespread kind of cardiovascular heart disease. Coronary
Artery Disease happens when the coronary arteries become
narrowed, and the blood supply to the heart muscles is not
enough.
Heart Disease is the most important and major cause of death
worldwide nowadays. Coronary arteries are the structure or
a network that is used for oxygen supply rich blood from the
entire heart muscle. It may cause swear pain and heart attack.
"We required very professional medical specialists for this
cure because a diagnosis of the heart disease is not easy" [1].
Nowadays, heart disease is a hot health topic in our daily life,
this type of disease that a cause of heart failure and affects the
human heart and blood vessels. "A heart is the most
important organ in body structure. For instance, if its
working is not properly, it will become a cause and damage
the other organ of the human body like coronary arteries,
brain, kidney, etc. This risk factor of heart disease is
increased by High blood pressure bp, Unhealthy diet,
Smoking, High cholesterol, Diabetics, Consuming immense
alcohol, coronary infection, being overweight, hypertension"
[2].
Symptoms of Heart attack:
• Shortness of breath
• Pain may travel to the left or right arm or neck
• Fatigue
• Rapid or irregular heartbeat
• Cold sweat and unsteadiness
• Coughing or wheezing
Types of cardiovascular disease:
• Inherited heart disease.
• Heart attack.
• Stroke and more.
• Coronary artery disease (CAD) Coronary artery
becomes narrowed
• Vascular disease (blood vessel disease).
Figure 2. Number of Deaths by Cause Worldwide 2017,
World Health Organization (WHO) 2018.
III. LITERATURE REVIEW
H. Benjamin et al. [4], in their work on the "supervised
machine learning" concept used to find the predictions of
heart disease, have used the following data mining
classification algorithms for analysis and prediction, namely,
Naïve Bayes, Random Forest, and Decision Tree. They have
proposed by experimental results and proved that Random
Forest gives better result performance as compare to Naïve
Bayes and Decision tree. In this research work, the dataset is
sourced from the data source StatLog for creating heart
disease prediction.
INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 08 ISSUE: 04 | APR 2021 WWW.IRJET.NET P-ISSN: 2395-0072
© 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3541
Senthilkumar Mohan et al. [5], Hybrid Random Forest, and
novel method by using Linear Model (HRFLM) and their goals
to finding the important features by using Machine learning's
techniques and increase the performance and accuracy for
heart disease prediction. Research work core aims to process
raw data through different steps and deliver a new respective
novel judgment of heart disease prediction. Their prediction
model is presented by various combinations of features and
numerous recognized classification methods to increase the
accuracy performance result. They have done work on many
classification models to predict cardiovascular heart disease
and compared their accuracy. They have proposed a
comparison with HRFLM. Dataset used by UCI ML repository,
and their approach claimed an accuracy level of 88.7%.
Mohammad Shafenoor Amin et al. [6], in their work they
suggested data mining classification methods for predicting
the heart disease result. The proposed testing was used to
classify important features by using data mining techniques.
The Cleveland dataset was collected from the UCI ML
Repository for heart disease prediction. They have used some
data mining techniques, namely SVM, DT, K-NN, LR, Naïve
Bayes, vote, and Neural network. They have also performed
experiments on another dataset using the UCI Statlog data set
to identify the verdicts. The maximum accuracy gain results
for heart disease diagnostic system can proficiently predict
the danger level of heart disease in the future. Their approach
claimed maximum accuracy was accomplished by SVM.
Ching-seh Mike Wu et al. [7], Heart disease is a regular
problem globally, and the death rate is very high due to heart
diseases and increases day by day. "Nowadays,
cardiovascular heart disease (CHD) is the main cause of
human deaths in the whole world. In this research work, they
have used different classifier data mining techniques. Test
dataset scraped from the UCI repository, and there are 13
attributes of the patient. They have tested various
experiments. Logistic regression and Naive Bayes predicted
the maximum accuracy when they used a huge dataset.
Decision Tree and Random Forest give an enhanced result on
the small dataset". They have proposed that a random forest
shows well accuracy performance than a decision tree.
Jagdeep Singh et al. [8], They performed work by using
different association and classification methods to predict the
heart disease dataset gathered from the UCI ML Repositories.
Apriori and FPGrowth are used here to find some heart
disease dataset association rules to predict cardiovascular
heart disease (CHD Cleveland dataset used here, a total of 313
occurrences and 13 attributes. Core work of this research
work is present to attain high accuracy for earlier diagnoses
of (CHD), They have proposed hybrid associative
classification using Waikato Environment for Knowledge
Analysis (WEKA) environment and claimed that the highest
accuracy was achieved by using IBK (Nearest Neighbor) with
Apriori associative algorithms.
Nathaniel David Oye et al. [9], "Heart Disease Prediction using
Machine Learning and Data Mining Techniques," namely as
the Decision Tree, Naive Bayes, and Artificial Neural Network
(ANN) to predict heart diseases. They observed that most
studies based on the Cleveland heart disease (CHD) data set
normally hold 303 occurrences and 13-14 attributes.
According to their research work and observation, this
dataset is so small and restricted with limited heart disease
features. They proposed that there should be a further
composite model that joins many geographical data sources
to maximize the precision of predicting the primary trending
of heart disease.
Abhishek Rairikar et al. [10], they have suggested a well-
organized method for predicting heart disease, they applied
different data mining techniques, namely KNN, Decision trees
(DT), and Naive Bayes (NB). They built an effective method
for Diagnosing heart attack results through GUI form. They
proposed from results that KNN provides better accuracy
than Naive Bayes and Decision tree.
IV. METHODOLOGY
A. Data Source
This research work dataset is sourced from the UCI ML
repository. The following four databases in UCI ML
Repository are Switzerland, Hungary, Cleveland, and
the VA LB. Cleveland database is mostly used here in
this research because the Cleveland database is the
most useable database by ML researchers and with
complete records. The dataset contains 303 instances,
and this database contains 76 attributes with the
suitable 14 clinical parameters [14]. There are total 14
attributes, but 1 attribute is used as the projected
attribute for heart disease. A dataset's clinical attribute
is referred to as tests related to heart disease, i.e., chest
pain (cp) type, blood pressure (bp), blood sugar level,
electrocardiographic result, etc. All attributes and
features with their descriptions values are shown in
Table 1. After data pre-processing, data has been
converted from Numeric to Nominal. In Figure 4, the
percentage of patients who have not heart disease is
45.54%, and the percentage of patients having heart
disease is 54%.46.
INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 08 ISSUE: 04 | APR 2021 WWW.IRJET.NET P-ISSN: 2395-0072
© 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3542
TABLE 1. ATTRIBUTES AND DESCRIPTION OF THE DATASET FROM UCI
CLEVELAND DATASET
S.
N
O.
Attribute
name
Description
Type
1
Age
Patients age in years
Numeric
2
Sex
1 for male
0 for female
Nominal
3
Cp
Chest pain type
Nominal
4
Trestbpd
Resting blood pressure:
92-200
Numeric
5
Chol
Serum cholesterol in mg/dl
Numeric
6
Fbs
Fasting blood sugar level
1 if true
0 if false
Nominal
7
Restecg
Resting
electrocardiographic
results in 3 values;
Value 0: normal
Value 1: having ST-T wave
abnormality
Value 2: showing probable
or definite left ventricular
hypertrophy by Estes'
criteria
Nominal
8
Thalach
Maximum heart rate
achieved
Numeric
9
Exang
Exercise induced angina
(1 for yes and 0 for no)
Nominal
10
Oldpeak
ST depression induced by
exercise
71-202
Numeric
11
peakSlope
The slope of the peak
exercise ST segment
Value 1: up sloping
Value 2: flat
Value 3: down sloping
Numeric
12
Ca
Number of major vessels
(0-3) colored by
fluoroscopy
Numeric
13
Thal
The heart status is
described with 3 values.
Value 3: normal
Value 6: fixed defect
Value 7: reversible defect
Nominal
14
Disease
It represents the diagnosis
of heart disease with 5
values.
0 meaning absence
1-4 indicate presence of
Nominal
heart disease
Figure 3: Distribution of "numbers" in UCI ML Cleveland
dataset.
B. Architecture Diagram:
Figure 4. Experiment workflow with UCI dataset.
C. Description of Algorithms
The following six data mining classification techniques
used here, namely Decision Tree, SVM, K-NN, Naive
Bayes, Logistic Regression, and Random Forest, are used
to analyze the dataset.
a) Decision Tree:
It is based on a supervised learning technique and
decision tree used for classification and regression
models. This is a very common algorithm used for
classifications [13]. The decision tree aims to
generate a model for predicting the value of an
objective variable. Decision tree flowchart-like
structure helps us decide by learning some simple
decision rules. Here each node acts as a test case for
some features and each leaf node provides the
outcome and it shows definite results like true or
INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 08 ISSUE: 04 | APR 2021 WWW.IRJET.NET P-ISSN: 2395-0072
© 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3543
false and 1 or 0 etc.
b) K-Nearest Neighbors (KNN):
It is a supervised ML algorithm that is used for
regression and classification problems. It is
normally used for classification predictive
problems. KNN helps us to classify the data into
various groups. KNN implementation is very simple,
but it is a lazy learning algorithm and creates no
earlier supposition.
c) Support Vector Machine (SVM):
The main motive of SVM is to discover the
hyperplane which divides the two classes of the
dataset it is an ML algorithm and is used to
categorize the dataset. SVM sorts the data into one
of two categories. A hyperplane is divided into two
classes with the maximum distance it is known as
ideal hyperplane of the form f(x) = (wt x + b ), It will
show low performance if the given data is noisy.
d) Random Forest:
It is a cooperative learning method for classification
it consists of many decision trees based on the
parent's tree and Integrates all of them to get the
best results. It can handle a huge amount of data
easily and efficiently work on large data. For
instance a given data, X = { x1, x2, ….. , xn} with
reactions to Y = { x1, x2, ….. , xn} and it recurrences
the getting from b = 1 – B.
V. PROPOSED METHOD
Following assorted earlier studies [1, 6, 4, 11], various
authors have discussed predicting the significant features of
heart disease prediction by using different machine learning
and data mining techniques. We proposed a Logistic
regression machine learning technique for heart disease
prediction of significant features. This proposed model for the
heart disease prediction method is introduced for deep
learning algorithms and perspectives. After pre-processing
the dataset Logistic Regression, a data mining classification
technique was applied here by using the Sklearn library to
analyze the score. Implementation of the Naïve Bayes method
of getting accuracy results, and this classification results
section done by using Python. Finally, at the end, compare the
Comparing Model and Confusion Matrix results. Firstly we
imported the data that contain different variable like gender,
age, cp(chest pain), sex, slope, target, etc. After the
accessibility of the data, we created a predictive model based
on the Logistic Regression algorithm. This classified data
based on various organized features of heart disease patients.
Create a Logistic Regression model with the help of
temporary variables and used the sigmoid function for
graphical representation classified dataset.
a) Logistic Regression:
The logistic regression is a classification algorithm of
Machine Learning (ML). It is used to predict the
probability of the dependent variable. It also provides
high accuracy and here first of all data should be
imported and then it can be trained for prediction. It is
logistic regression, and it is also presented by sigmoid
functions which help to show a good representation of
the graph. The dependent variables are the binary
variables that hold the coded data as 1(good, yes, pass,
etc.) or 0 (bad, no, fail, etc.).
b) Naïve Bayes:
It is a supervised classifier algorithm of ML that
categorizes the dataset. It is used to classify data into
predefined classes. It uses conditional probability to
classify the test dataset and this model applies Bayes
rules by independent features. Naïve Bayes classifier
finds the probability of each feature. It requires a small
number of datasets and fasts to predict the class of test
data. It does not show results if the features are
correlated [14].
VI. RESULTS AND DISCUSSIONS
The proposed system has a Cleveland Heart Disease dataset
which is used to classified whether the patients have heart
disease or not according to their features. The overall
records in the dataset are distributed into two category
training and testing data sets. Logistic regression can also be
considered a sigmoid function. The sigmoid function is
usually used to denote precisely the logistic function; by
using this, we can easily represent our graphs and charts any
real value to the range 1 or 0. The proposed system is
applied to this data and tries to create an accurate model
that predicts (Data exploration and reading data) if the
patients have this disease or not. Figure 7 shows the whole
process of fixing the targets of heart disease. The percentage
of patients having heart disease is 54.46%, and the
percentage of patients who have not heart disease is
45.54%, where 0 shows the absence and 1 shows the
presence of heart disease. Cleveland Heart Disease dataset is
also classified into two categories for male and female
patients. Male patients are 68.32%, and females are 31.68%.
INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 08 ISSUE: 04 | APR 2021 WWW.IRJET.NET P-ISSN: 2395-0072
© 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3544
Figure 5. Implementation of Logistic Regression Algorithm
Figure 6. Reading Heart disease data set
Figure 7. Fixing the respective targets about heart
disease
Figure 8. Classifying the heart disease
Figure 9. Accuracy output in logistic regression algorithm
A confusion matrix is normally used to measure the
performance of respective algorithms, and it is also used to
measure the performance of classification. There are some
following the most basic terms for the confusion matrix.
TP (True Positive): It means the amount of records relates
to yes, they have the disease.
TN (True Negative): it means the amount of the record
relates to no. They don't have the disease, we predicted no.
FP (False Positives): We predicted yes, but they don't
actually have the disease.
INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 08 ISSUE: 04 | APR 2021 WWW.IRJET.NET P-ISSN: 2395-0072
© 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3545
FN (False Negative): We predicted no, but they actually do
have the disease.
Table 2: Confusion Matrix
Predicted
Negative(0)
Predicted
Positive(1)
Actual
Negative(0)
TP
FP
Actual
Positive(1)
FN
TN
Table 03: Confusion matrix for Logistic Regression
Class 0
Class 1
Class 0
188
22
Class 1
11
91
Training Set
Class 0
Class 1
Class 0
32
9
Class 1
3
17
Testing Set
a. Logistic Regression accuracy for training set
((118+91)/(11+22+118+91))*100 = 86.89%
b. Logistic Regression accuracy for the testing set
((32+17)/(3+9+32+17))*100 = 80.32%
VII. EVALUATION RESULTS
The proposed method works best result in 86.89% accuracy
by using a logistic regression algorithm. This work is done by
some different steps shown in Figure 4. The heart disease
prediction model's accuracy developed using 14 significant
attributes that are defined in Table 1. And table 2
summarized the accuracy results of specific classification
models that are obtained from proposed methods and by
other various authors. Experiment works display that the
heart disease prediction model developed using the identified
significant features and the best-performing technique
(Logistic regression) achieves an accuracy of 86.89% in heart
disease prediction.
TABLE 4. Accuracy comparison on the heart diseases
dataset by various authors with proposed model.
Author
Techniques &
Methodology
Accuracy
%
H.Benjamin Fredrick
David et.al [4]
Random Forest
81
S Anitha et.al [11]
KNN
76.67
Senthilkumar Mohan
et.al [5]
Decision Tree
78.69
M.A.Jabbar et.al [1]
Naïve Bayes
78.56
M. Shafenoor Amin
et.al [6]
SVM
80.98
Proposed Method
Logistic Regression
86.89
Figure 10. Accuracy Representation in Graph with
their respective algorithms
VIII. CONCLUSION
In this research work, various machine learning & data
mining classification techniques are used to analyze and
predict heart disease accuracy. Heart disease nowadays is a
hot health topic in our daily life and it is one-third of all
deaths in the whole world. The proposed system applied on
this data and tried to create an accurate model that predicts
(Data exploration and reading data) if the patients have this
disease. The core purpose of this work is the prediction of
INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 08 ISSUE: 04 | APR 2021 WWW.IRJET.NET P-ISSN: 2395-0072
© 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3546
heart disease with a maximum amount of accuracy. Dataset is
collected from UCI ML repository. Cleveland database is used
here for heart disease prediction. After pre-processing the
dataset Logistic Regression, a data mining classification
technique was applied here by using the Sklearn library to
analyze the score. Proposed approach Logistic regression
achieved an accuracy of level of 86.89%.
IX. FUTURE SCOPE
In the future, the proposed system with data mining and ML
classification algorithm can be used for the prediction of
other diseases in the medical field. It provides us good
accuracy by observing some different research papers. And
there are many strategies to improve this research and
address the boundaries of this study. This research work can
be extended by conducting the same experiment on a large-
scale real-life dataset. This work's coming future scope is the
prediction of heart diseases by using innovative techniques
and algorithms in minimum time complexity.
REFERENCES
[1] M.A.Jabbar, B.L. Deekshatulu and Priti Chandra, 2015.
Prediction of heart disease using Random forest and
Feature subset selection, AISC SPRINGER, vol 424,
pp187-196.
[2] Mr.Santhana Krishnan.J and Dr.Geetha.S, 2019. Prediction
of Heart Disease Using Machine Learning Algorithms,
(ICIICT) IEEE, 2019. DOI:
10.1109/ICIICT1.2019.8741465.
[3] Cincy Raju, Philipsy E, Siji Chacko, L Padma Suresh, Deepa
Rajan S, 2018. A Survey on Predicting Heart Disease using
Data Mining Techniques, IEEE Conference on Emerging
Devices and Smart Systems (ICEDSS 2018).DOI
10.1109/ICEDSS.2018.8544333.
[4] H.Benjamin Fredrick David and S. Antony Belcy, 2018.
Heart Disease Prediction Using Data Mining Techniques,
Ictact Journal On Soft Computing, Volume: 09, Issue: 01.
DOI: 10.21917/ijsc.2018.0253.
[5] Senthilkumar Mohan, Chandrasegar Thirumalai and
Gautam Srivastava, 2019. Effective Heart Disease
Prediction Using Hybrid Machine Learning Techniques,
Computer Science (IEEE Access) Vol 7, pp 81542-
81554(2019).DOI 10.1109/ACCESS.2019.2923707.
[6] Mohammad Shafenoor Amin, Yin Kia Chiam and Kasturi
Dewi Varathan, 2019. Identification of significant features
and data mining techniques in predicting heart disease,
Telematics and Informatics, Vol 36, pp 82-93. DOI
10.1016/j.tele.2018.11.007 .
[7] Ching-seh (Mike) Wu, Mustafa Badshah and Vishwa
Bhagwat, 2019, Heart Disease Prediction Using Data
Mining Techniques, 2nd International Conference on Data
Science and Information Technology July 2019, Pp 7–11,
DOI 10.1145/3352411.3352413.
[8] Jagdeep Singh, Amit Kamra and Harbhag Singh, 2016.
Prediction of Heart Diseases Using Associative
Classification, 5th International Conference on Wireless
Networks and Embedded Systems (WECON). DOI
10.1109/WECON.2016.7993480.
[9] Lamido Yahaya, Nathaniel David Oye and Etemi Joshua
Garba, 2020. A Comprehensive Review on Heart Disease
Prediction Using Data Mining and Machine Learning
Techniques, American Journal of Artificial Intelligence,
Volume 4, Pages: 20-29. DOI:
10.11648/j.ajai.20200401.12.
[10] Abhishek Rairikar, Vedant Kulkarni, Vikas Sabale,
Harshavardhan Kale and Anuradha Lamgunde 2017 ,Heart
Disease Prediction Using Data Mining Techniques, (IEEE)
International Conference on DOI:
10.1109/I2C2.2017.8321771.
[11] S Anitha and NSridevi, 2019. Heart disease prediction
using data Mining Techniques, Journal of Analysis and
Computation, hal-02196156.
[12] J. Ross Quinlan, 1986. Induction of Decision Trees,
Machine Learning, Vol. 1, No. 1, pp. 81-106,.
[13] M.A. Jabbar, 2018. Heart disease prediction system based
on hidden naviebayes classifier. International conference
on circuits, controls, communications and computing(14C)
[14] Cox, David R., 1958. The regression analysis of binary
sequences. Journal of the Royal Statistical Society. Series B
(Methodological) 215-242
[15] https://archive.ics.uci.edu/ml/machine-learning
databases/heart-disease/
