Early Exposing Cardiovascular Disease Identification Using Machine Learning Approach

Md Khairul Islam; Syeda Jannatul Boshra; Mahfuzur Rahman; Md Nabir Hossain; Abdus Sattar

doi:10.1109/ICCCNT54827.2022.9984250

Early Exposing Cardiovascular Disease Identification Using Machine Learning Approach

Md Khairul Islam^*
, Syeda Jannatul Boshra
, Mahfuzur Rahman
, Md Nabir Hossain
, Abdus Sattar

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Nowadays Cardiovascular disease is most important topics in medical sector. Numerous people all over the world affected with this disease and it is going alarming to the world population. The main goal of this study to predict the heart disease probability, find out the best algorithms for these types of data set and implement statistical analysis to find the relation among the attributes. While doing prediction analysis, we used some popular machine learning algorithms such as Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB) and Random Forest (RF). Decision Tree and Random Forest algorithms are gave the best accuracy rate among the algorithms and both are exactly same 96%. The statistical analysis of this study is clearly demonstrate that, population with higher heart rate, typical angina and non-anginal pain are most affected in disease. Hopefully, this study will be helpful for medical researchers and make decision for best machine learning algorithm.

Original language	English
Title of host publication	2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665452625
DOIs	https://doi.org/10.1109/ICCCNT54827.2022.9984250
State	Published - 2022
Externally published	Yes
Event	13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022 - Kharagpur, India Duration: 3 Oct 2022 → 5 Oct 2022

Publication series

Name	2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

Conference

Conference	13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022
Country/Territory	India
City	Kharagpur
Period	3/10/22 → 5/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Decision Tree
Heart Disease
Machine Learning
Random Forest

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Information Systems and Management
Safety, Risk, Reliability and Quality
Media Technology

Access to Document

10.1109/ICCCNT54827.2022.9984250

Cite this

Islam, M. K., Boshra, S. J., Rahman, M., Hossain, M. N., & Sattar, A. (2022). Early Exposing Cardiovascular Disease Identification Using Machine Learning Approach. In 2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022 (2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCCNT54827.2022.9984250

Islam, Md Khairul ; Boshra, Syeda Jannatul ; Rahman, Mahfuzur et al. / Early Exposing Cardiovascular Disease Identification Using Machine Learning Approach. 2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022).

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title = "Early Exposing Cardiovascular Disease Identification Using Machine Learning Approach",

abstract = "Nowadays Cardiovascular disease is most important topics in medical sector. Numerous people all over the world affected with this disease and it is going alarming to the world population. The main goal of this study to predict the heart disease probability, find out the best algorithms for these types of data set and implement statistical analysis to find the relation among the attributes. While doing prediction analysis, we used some popular machine learning algorithms such as Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Na{\"i}ve Bayes (NB) and Random Forest (RF). Decision Tree and Random Forest algorithms are gave the best accuracy rate among the algorithms and both are exactly same 96\%. The statistical analysis of this study is clearly demonstrate that, population with higher heart rate, typical angina and non-anginal pain are most affected in disease. Hopefully, this study will be helpful for medical researchers and make decision for best machine learning algorithm.",

keywords = "Decision Tree, Heart Disease, Machine Learning, Random Forest",

author = "Islam, \{Md Khairul\} and Boshra, \{Syeda Jannatul\} and Mahfuzur Rahman and Hossain, \{Md Nabir\} and Abdus Sattar",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022 ; Conference date: 03-10-2022 Through 05-10-2022",

year = "2022",

doi = "10.1109/ICCCNT54827.2022.9984250",

language = "English",

series = "2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022",

address = "United States",

}

Islam, MK, Boshra, SJ, Rahman, M, Hossain, MN & Sattar, A 2022, Early Exposing Cardiovascular Disease Identification Using Machine Learning Approach. in 2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022. 2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022, Institute of Electrical and Electronics Engineers Inc., 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022, Kharagpur, India, 3/10/22. https://doi.org/10.1109/ICCCNT54827.2022.9984250

Early Exposing Cardiovascular Disease Identification Using Machine Learning Approach. / Islam, Md Khairul; Boshra, Syeda Jannatul; Rahman, Mahfuzur et al.
2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Early Exposing Cardiovascular Disease Identification Using Machine Learning Approach

AU - Islam, Md Khairul

AU - Boshra, Syeda Jannatul

AU - Rahman, Mahfuzur

AU - Hossain, Md Nabir

AU - Sattar, Abdus

PY - 2022

Y1 - 2022

N2 - Nowadays Cardiovascular disease is most important topics in medical sector. Numerous people all over the world affected with this disease and it is going alarming to the world population. The main goal of this study to predict the heart disease probability, find out the best algorithms for these types of data set and implement statistical analysis to find the relation among the attributes. While doing prediction analysis, we used some popular machine learning algorithms such as Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB) and Random Forest (RF). Decision Tree and Random Forest algorithms are gave the best accuracy rate among the algorithms and both are exactly same 96%. The statistical analysis of this study is clearly demonstrate that, population with higher heart rate, typical angina and non-anginal pain are most affected in disease. Hopefully, this study will be helpful for medical researchers and make decision for best machine learning algorithm.

AB - Nowadays Cardiovascular disease is most important topics in medical sector. Numerous people all over the world affected with this disease and it is going alarming to the world population. The main goal of this study to predict the heart disease probability, find out the best algorithms for these types of data set and implement statistical analysis to find the relation among the attributes. While doing prediction analysis, we used some popular machine learning algorithms such as Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB) and Random Forest (RF). Decision Tree and Random Forest algorithms are gave the best accuracy rate among the algorithms and both are exactly same 96%. The statistical analysis of this study is clearly demonstrate that, population with higher heart rate, typical angina and non-anginal pain are most affected in disease. Hopefully, this study will be helpful for medical researchers and make decision for best machine learning algorithm.

KW - Decision Tree

KW - Heart Disease

KW - Machine Learning

KW - Random Forest

UR - https://www.scopus.com/pages/publications/85146355776

U2 - 10.1109/ICCCNT54827.2022.9984250

DO - 10.1109/ICCCNT54827.2022.9984250

M3 - Conference contribution

AN - SCOPUS:85146355776

T3 - 2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

BT - 2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

Y2 - 3 October 2022 through 5 October 2022

ER -

Islam MK, Boshra SJ, Rahman M, Hossain MN, Sattar A. Early Exposing Cardiovascular Disease Identification Using Machine Learning Approach. In 2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022). doi: 10.1109/ICCCNT54827.2022.9984250

Early Exposing Cardiovascular Disease Identification Using Machine Learning Approach

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this