Prediction of cardiovascular disease with machine learning

Prediction of Cardiovascular
disease with Machine Learning
By
Pravin Landge
10/16/2020 1

Contents
• Objective
• Introduction to heart disease
• The dataset and attributes
• Classification system
• Conclusion
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Introduction
• Heart disease describes a range of conditions that
affect your heart. Diseases under the heart disease
umbrella include blood vessel diseases, such as
coronary artery disease, heart rhythm problems
(arrhythmias) and heart defects you’re born with
(congenital heart defects)
• The term “heart disease” is often used interchangeably
with the term “cardiovascular disease”. Cardiovascular
disease generally refers to conditions that involve
narrowed or blocked blood vessels that can lead to a
heart attack, chest pain (angina) or stroke
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• Prediction of cardiovascular disease is
regarded as one of the most important
subjects in the section of clinical data analysis.
• The amount of data in the healthcare industry
is huge. Data mining turns the large collection
of raw healthcare data into information that
can help to make informed decisions and
predictions.
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Motivation
• But it is difficult to identify heart disease because
of several contributory risk factors such as
diabetes, high blood pressure, high cholesterol,
abnormal pulse rate, and many other factors. Due
to such constraints, scientists have turned
towards modern approaches like Data Mining and
Machine Learning for predicting the disease.
• Machine learning (ML) proves to be effective in
assisting in making decisions and predictions
from the large quantity of data produced by the
healthcare industry.
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• The dataset used in this article is the
Cleveland Heart Disease dataset taken from
the UCI repository.
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Dataset attributes
• There are 14 columns in the dataset, which are described below.
1. Age: displays the age of the individual.
2. Sex: displays the gender of the individual using the following format :
1 = male
0 = female
3. Chest-pain type: displays the type of chest-pain experienced by the individual
using the following format :
1 = typical angina
2 = atypical angina
3 = non — anginal pain
4 = asymptotic
4. Resting Blood Pressure: displays the resting blood pressure value of an individual
in mmHg (unit)
5. Serum Cholestrol: displays the serum cholesterol in mg/dl (unit)
6. Fasting Blood Sugar: compares the fasting blood sugar value of an individual
with 120mg/dl.
If fasting blood sugar > 120mg/dl then : 1 (true)
else : 0 (false)
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7. Resting ECG : displays resting electrocardiographic results
0 = normal
1 = having ST-T wave abnormality
2 = left ventricular hyperthrophy
8. Max heart rate achieved : displays the max heart rate achieved by an individual.
9. Exercise induced angina :
1 = yes
0 = no
10. ST depression induced by exercise relative to rest: displays the value which is an integer or float.
11. Peak exercise ST segment :
1 = upsloping
2 = flat
3 = downsloping
12. Number of major vessels (0–3) colored by flourosopy : displays the value as integer or float.
13. Thal : displays the thalassemia :
3 = normal
6 = fixed defect
7 = reversible defect
14. Diagnosis of heart disease : Displays whether the individual is suffering from heart disease or not
:
0 = absence
1, 2, 3, 4 = present.
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Conclusion
• Heart Disease is one of the major concerns for
society today.
• It is difficult to manually determine the odds
of getting heart disease based on risk factors.
However, machine learning techniques are
useful to predict the output from existing
data.
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References
• Johnson KW, Shameer K, Glicksberg BS, Readhead B, Sengupta PP,
Bjorkegren JLM, Kovacic JC, Dudley JT. Enabling precision cardiology
through multiscale biology and systems medicine. JACC Basic Transl
Sci. 2017;2:311–327.
• Shameer K, JohnsonKW, Glicksberg BS, Dudley JT, Sengupta
PP.Machine learning in cardiovascular medicine: are we there yet?
Heart. 2018;104:1156–1164
• Sengupta PP, Shrestha S. Machine learning for data-driven
discovery: the rise and relevance. JACC Cardiovasc Imaging.
2019;12:690–692.
• Seetharam K, Shrestha S, Sengupta PP. Artificial intelligence in
cardiovascular medicine. Curr Treat Options Cardiovasc Med.
2019;21:25.
• Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl
J Med. 2019;380:1347–1358
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Prediction of cardiovascular disease with machine learning

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