2020 International Conference on Computer Communication and Informatics (ICCCI -2020), Jan. 22-24, 2020, Coimbatore, INDIA
978-1-7281-4514-3/20/$31.00 ©2020 IEEE
Estimation of Prediction for Getting Heart Disease Using
Logistic Regression Model of Machine Learning
Montu Saw, Tarun Saxena, Sanjana Kaithwas, Rahul Yadav, Nidhi Lal
Dept. of Computer Science and Engineering
IIIT Nagpur, India
[email protected],
[email protected],[email protected],
[email protected], [email protected]
Abstract-In the current era deaths due to heart disease have
become a major issue. Approximately one person dies per
minute due to heart disease. Data is generated and has to be
stored daily because of fast growth in Information Technology.
The data which is collected is converted into knowledge by data
analysis by using various combinations of algorithms.
Healthcare professionals working in the area of cardiac disease
have their own limits and can not forecast the probability of
high accuracy in cardiac disease .This paper aims to improve
Heart Disease predict accuracy using the Logistic Regression
model of machine learning considering the health care dataset
which classifies the patients whether they are having heart
diseases or not according to the information in the record.
Keywords:-Heart Diseases;Data Analysis; Machine Learning;
Logistic Regression Algorithms.
I. INTRODUCTION
The load of cardiovascular diseases is rapidly increasing all
over the world from the past few years. Even if these diseases
has found as the most important source of death, it has been
announced as the most manageable and avoidable
disease[1].Mainly, blockage in arteries causes heart stroke. It
occurs when heart does not pump the blood around the body
efficiently.
Having high blood pressure is also one of the main causes of
getting a heart disease. A survey says that, in 2011 to 2014,
the commonness of hypertension in the world was about 35%,
which is also a cause of heart disease. Similarly, there are
many more reasons for getting a heart disease such as obesity,
not taking in proper nutrition, increased cholesterol and lack
of physical activity. So, prevention is very necessary. For
prevention, awareness of heart diseases is important. Around
47% of people dies outside the hospital and it shows that they
don’t act on early warning signs.
Nowadays, lifespan of a human being is reduced because of
heart diseases. So, World Health Organization (WHO)
developed targets for prevention of non-communicable
diseases (NCDs) in 2013, in which, 25% of the relative
reduction is due to cardiovascular diseases and at least 50% of
patients with cardiovascular diseases are expected to have
access to appropriate medicines and medical advice by
2025[2]. Around 17.9 million people died just because of
cardiovascular diseases in 2016, which is 31% of deaths
around the world.
A major challenge in heart diseases is its detection[3]. It is
difficult to predict that a person has a heart disease or not.
There are devices available that can foresee heart disease, but
they are either expensive or not effective in calculating human
chance of heart disease[4]. According to a survey conducted
by the World Health Organization (WHO), medical
professionals can predict just 67% of heart disease, so there is
a wide range of research in this area[5]. In case of India,
access to good doctors and hospitals in rural areas is very low.
A 2016 WHO report says that, just 58% of the doctors have
medical degree in urban areas and 19% in rural areas.
In USA, someone has a heart attack every 40 seconds, that is,
more than one person dies in USA due to heart attack. At 712
deaths per 100,000 people, Turkmenistan also has the highest
death rate until 2012. Kazakhstan, on the other hand, has the
second highest death rate from heart disease. India is ranked
56th in this series[6]. Study also shows that, at ages 30-69
years, 1.3 million cardiovascular deaths, 0.9 million (68.4%)
were caused by coronary heart disease and 0.4 million (28.0
%) by stroke
Heart diseases are a major challenge in medical science,
Machine Learning could be a good choice for predicting any
heart disease in humans[7]. Heart diseases can be predicted
using Neural Network, Decision Tree, KNN, etc. Later in this
paper, we will see that how Logistic Regressionis used to find
the accuracy for heart disease. It also shows that how ML will
help in our future for heart disease.
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2020 International Conference on Computer Communication and Informatics (ICCCI -2020), Jan. 22-24, 2020, Coimbatore, INDIA
978-1-7281-4514-3/20/$31.00 ©2020 IEEE
II. RELATED WORK
There are many works in literature which diagnoses heart
diseases using machine learning as well as data mining. A
brief survey of that is presented here. A paper named ‘A study
of cardiac disease using M’s approach to machine learning and
analytics. On September 2018, Marimuthu, M. Abinaya, K.S.
Hariesh, K. Mandhankumar, and V. Pavithra are released. The
result shows that, through the literature survey, they concluded
that, there is a need of combinational and more complex
models to increase the accuracy of prediction of heart
diseases.
Many articles that have been published about 2 to 3 years ago
are less accurate to predict heart disease compared to the
needs of today. ‘ Efficient prediction system for heart disease
using decision tree ‘ published by Sharma Purshottam et al in
2015. They have used decision tree classifier as their
technique and getting 86.3% accuracy. Similarly, we have,
‘Prediction of heart disease using modified K-means and by
using naïve bayes’ by Sairabi H Mujawar et al. This paper was
published in 2015. Their accuracy percentage for detection of
heart disease was 93% and for undetection it was 89% [13].
This shows that the accuracy percentage depends on the
technique which you are using.
Another example is of ‘heart disease prediction using machine
learning and data mining techniques’ by Jaymin Patel, Prof.
Tejpal Upadhyay and Dr. Sameer Patel from Nirma
University, Gujarat. [14]
III.EXISTING SYSTEM
Heart disease has even been emphasised as a silent killer with
no obvious symptoms leading to the person’s death. The preexisting system[6] works on deep learning as well as data
mining sets[7]. Clinical diagnosis plays a vital, yet
complicated, role that requires to be effectively and accurately
performed. Appropriate computer-based data and decision aid
should be helped to reduce the expense of clinical testing.
Data mining is the use of computer techniques to identify
patterns and reliability in data sets. Moreover, with the
emergence of data mining over the past two decades, there is a
great opportunity for computers to construct and classify the
different attributes or categories directly. Understanding the
risk components associated with heart disease allows
specialists in medical services to identify high-risk patients
with heart disease. Mathematical analysis revealed risk factors
correlated with heart disease such as age, blood pressure, total
cholesterol, diabetes, hypertension, heart disease family
history, obesity and lack of physical activity, fasting blood
sugar, etc.[8]
Fig.1: Flowchart of Proposed Work[10]
IV. PROPOSED SYSTEM
This proposed system has data which classifies if patients have
heart disease or not according to some parameters. This
proposed system can try to use this data to create a model that
tries to predict (reading data and data Exploration)[9]if a
patient has this disease or not. In this proposed system, using a
logistic regression (classification) algorithm we use thesklearn
library to calculate the score.Randomsearch is a technique
where random combinations of the hyperparameters are used
to find the best solution for the built model. Finally, analyzing
the results with the help of Comparing Models and Confusion
Matrix. From the data we are having, it is classified into
different structured data based on the features of the patient
heart. From the availability of the data, we have to create a
model that predicts the patient’s disease using a logistic
regression algorithm. First, we have to import datasets read the
datasets, the data should contain different variables like age,
gender, sex, chest pain, slope, target. The data should be
explored so that the information is verified. Create a
temporary variable and also build a model for logistic
regression[10].
Here, we use a sigmoid function which helps in
the graphical representation of the classified data. By using
logistic regression, the accuracy is increased as compared to
the previous work done in the existing system.
V.APPROACH AND METHODOLOGY
World Health Organization’s launch has documented 12
million deaths worldwide; due to heart disease every year.
Half of deaths are caused by cardio-vascular diseases in the
United States and other developed countries. Advanced
cardiovascular disease prognosis can help make decisions
about changes in lifestyle in high-risk patients and in turn
reduce complications. This work aims to classify the most
relevant / risk factors of heart disease as well as predict the
overall risk using Data Preparation logistic regression.
Logistic Regression is a form of regression analysis used in
statistics to predict the outcome of a categorical dependent
variable from a collection of predictors or independent
variables. In logistic regression the dependent variable is
always binary. Logistic regression is mainly used to for
prediction and also calculating the probability of
success.[11]
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2020 International Conference on Computer Communication and Informatics (ICCCI -2020), Jan. 22-24, 2020, Coimbatore, INDIA
978-1-7281-4514-3/20/$31.00 ©2020 IEEE
Fig.2: Dataset Distribution
Source: The dataset as shown in Fig.2 is from an ongoing
cardiovascular study on residents of the town of
Framingham, Massachusetts. The classification goal is to
predict whether the patient has 10-year risk of future
coronary heart disease (CHD). There are both demographic,
behavioral and medical risk factors that we can see in Fig.3.
Fig.3:10-year risk of coronary heart disease CHD
Fig.4: Patient’s hypertensive nature
Until the last few decades,
hypertension has been the most
important single identifiable risk factor
for heart failure. Due in part to
uncertainties in the documentation of heart failure, the lack of
systematic recording of arterial pressure prior to the onset and
treatment for heart failure, and the lack of systematic
visualization of epicardial coronary arteries clearly shown in
Fig.4[12 ], the issue has become less clear over recent years.
Fig.5:Cigarettes per day
Fig.5 shows the effect of heart cigarette consumption.
Smoking damages the heart and blood vessels very fast,
but for most smokers who stop smoking, the damage is
quickly repaired. Even Just a few cigarettes can harm the
heart, and stopping is the only tested strategy to keep the heart
healthy from smoking effects.
Fig.6:Glucose level
Studies found that high blood sugar (glucose) causes greater
blood vessel contraction, as well as recognizing a protein
associated with this increased contraction. The results may
lead to new therapies that are shown in Fig.6 to improve
outcomes after heart attack or stroke.
Fig.7:Age of the patient
Fig.7 shows the effect on
cardiovascular disease of
the age factor. Age is the
most important risk
factor in the
development of
cardiovascular and heart
disease, with about a
tripling of risk per
decade of life. During
adolescence, coronary
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2020 International Conference on Computer Communication and Informatics (ICCCI -2020), Jan. 22-24, 2020, Coimbatore, INDIA
978-1-7281-4514-3/20/$31.00 ©2020 IEEE
fatty streaks can start to form. 82% of people who die from
coronary heart disease are estimated to be 65 years of age or
older. At the same time, the threat of stroke doubles after age
55 per decade[13 ].
Fig.8: Dataset after Wrangling
Fig.9: Before Data Wrangling
Fig.10: After Data Wrangling
Fig.11: Accuracy Result
VI. RESULT
From the above statistics it is clear that the model is highly
specific than sensitive.Men seem to be more susceptible to
heart disease than women. Increase in age, number of
cigarettes smoked per day and systolic Blood Pressure also
show increasing odds of having heart disease.Total
cholesterol shows no significant change in the odds of CHD.
This could be due to the presence of good cholesterol
(HDL) in the total cholesterol reading. Glucose too causes a
very negligible change in odds (0.2%). The model predicted
with 0.87 accuracy which can be seen in Fig.11. The model
is more specific than sensitive. Overall model could be
improved with more data and by using more Machine
Learning models.
VII. CONCLUSION
The amount of Heart diseases can exceed the current scenario
to reach the maximum point. Heart disease are complicated
and each and every year lots of people are dying with this
disease.It is difficult to manually determine the odds of getting
heart disease based on risk factors previously shown. By using
this system one of the major drawbacks of this work is that it’s
main focus is aimed only to the application of classifying
techniques and algorithms for heart disease prediction, by
studying various data cleaning and mining techniques that
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2020 International Conference on Computer Communication and Informatics (ICCCI -2020), Jan. 22-24, 2020, Coimbatore, INDIA
978-1-7281-4514-3/20/$31.00 ©2020 IEEE
prepare and build a dataset appropriate for data mining so that
we can use this Machine Learning in that logistic regression
algorithms by predicting if patient has heart disease or not.
Any non-medical employee can use this software and predict
the heart disease and reduce the time complexity of the
doctors.It is still an open domain waiting to get implemented
in heart disease predication and increase the accuracy.
VIII. FUTURE WORK
Today’s, world most of the data is computerized and
everything is in the cloud which can be accessed although it is
not utilized properly. By analyzing the available data, we can
also use for unknown patterns. The primary motive of this
research is the prediction of heart diseases with high rate of
accuracy. For predicting the heart disease, we can use logistic
regression algorithm, sklearn in machine learning. The future
scope of the paper is the prediction of heart diseases by using
advanced techniques and algorithms in less time complexity.
IX. REFERENCES
[1] AvinashGolande, Pavan Kumar T. Heart disease prediction
using effective machine learning techniques.
[2] The Lancet Global Health. The changing patterns of
cardiovascular diseases and their risk factors in the states of
India: The global burden of disease study 1990-2016.
[3] Himanshu Sharma, M A Rizvi. Prediction of heart disease
using machine learning algorithms: A survey.
[4] World health ranking.
[5] Himanshu Sharma, M A Rizvi. Prediction of heart disease
using machine learning algorithms: A survey.
[6] Sana Bharti,2015. Analytical study of heart disease
prediction compared with different algorithms; International
conference on computing, communication, and automation
(ICCA2015).
[7] Monika Gandhi,2015. Prediction in heart disease using
techniques of data mining, International conference on
futuristic trend in computational analysis and knowledge
management (ABLAZE- 2015)
[8] SarathBabu, 2017.Heart disease diagnosis using data
mining technique, international conference on electronics,
communication and aerospace technology (ICECA2017)
[9] A H Chen, 2011. HDPS: heart disease prediction system;
2011 computing in cardiology
[10] Reddy Prasad,Pidaparthi Anjali, S.Adil, N.Deepa(Feb
2019) Heart Disease Prediction using Logistic Regression
Algorithm using Machine Learning
[11] Gritsenko, Elena. “Health Care Analytics: Modeling
Behavioral Risk Factors Associated With Disease.” (2019).
[12] Kazzam, E., Ghurbana, B., Obineche, E. et al.
Hypertension — still an important cause of heart failure?. J
Hum Hypertens19, 267–275 (2005)
doi:10.1038/sj.jhh.1001820
[13] M. Marimuthu, M. abinaya, K S Hariesh, K
Madhankumar, V Pavithra. A review on heart disease
prediction using machine learning and data analytics
approach.
[14] Jaymin Patel, Prof. Tejpal Upadhyay and Dr. Samir Patel.
Heart disease prediction using machine learning and data
mining technique.
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2020 International Conference on Computer Communication and Informatics (ICCCI -2020), Jan. 22-24, 2020, Coimbatore, INDIA
978-1-7281-4514-3/20/$31.00 ©2020 IEEE
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