Authors :
Manak; Manaswi; Pankaj Kumar; Garima Gupta
Volume/Issue :
Volume 7 - 2022, Issue 5 - May
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3HCDuHP
DOI :
https://doi.org/10.5281/zenodo.6670625
Abstract :
This document explores the possibility of the
prediction whether a person is susceptible to various
heart diseases like Coronary Artery Disease (CAD),
Heart Arrhythmias, Heart Failure, Heart Valve Disease,
Pericardial Disease, Cardiomyopathy (Heart Muscle
Disease), Congenital Heart Disease and many more
which has put a great threat to human beings given how
our lives and schedules are evolving into more sedentary
ones with the advent of technologies originally made to
make our lives easier. A passive lifestyle puts not only
our heart at risk, but also is a direct cause of more
physical and mental illnesses and diseases like
osteoporosis, lipid disorders diabetes, depression and
anxiety, and increase the risks of different types of
cancer and a higher blood pressure. In this article, we
have aimed to study the various different factors that
may or may not be in direct correlation of heart diseases.
These factors are as follows: Age, sex, chest pain type,
resting blood pressure, cholesterol in mg/dl, fasting
blood sugar, resting electrocardiography results,
maximum heart rate achieved, exercise induced angina,
ST depression induced by exercise, slope of the peak
exercise ST segment, number of major vessels and
maximum heart rate. We have also compared the
correlation of these factors with the possibility of a heart
related illness. These factors are elaborated in a more
detailed way in this paper. And for the same, we have
used multiple algorithms (logistic regression, naïve
bayes, Support vector machine, KNN, decision tree,
random forest and artificial neural network) and
compare the results to find out the most accurate one.
We are using dataset from kaggle.com.
Keywords :
angina, heart diseases, random forest, heart diseases prediction, classification, ensemble.
This document explores the possibility of the
prediction whether a person is susceptible to various
heart diseases like Coronary Artery Disease (CAD),
Heart Arrhythmias, Heart Failure, Heart Valve Disease,
Pericardial Disease, Cardiomyopathy (Heart Muscle
Disease), Congenital Heart Disease and many more
which has put a great threat to human beings given how
our lives and schedules are evolving into more sedentary
ones with the advent of technologies originally made to
make our lives easier. A passive lifestyle puts not only
our heart at risk, but also is a direct cause of more
physical and mental illnesses and diseases like
osteoporosis, lipid disorders diabetes, depression and
anxiety, and increase the risks of different types of
cancer and a higher blood pressure. In this article, we
have aimed to study the various different factors that
may or may not be in direct correlation of heart diseases.
These factors are as follows: Age, sex, chest pain type,
resting blood pressure, cholesterol in mg/dl, fasting
blood sugar, resting electrocardiography results,
maximum heart rate achieved, exercise induced angina,
ST depression induced by exercise, slope of the peak
exercise ST segment, number of major vessels and
maximum heart rate. We have also compared the
correlation of these factors with the possibility of a heart
related illness. These factors are elaborated in a more
detailed way in this paper. And for the same, we have
used multiple algorithms (logistic regression, naïve
bayes, Support vector machine, KNN, decision tree,
random forest and artificial neural network) and
compare the results to find out the most accurate one.
We are using dataset from kaggle.com.
Keywords :
angina, heart diseases, random forest, heart diseases prediction, classification, ensemble.
