Heart Disease Prediction using Machine Learning Techniques

Reldean Williams; Thokozani Shongwe; Ali N. Hasan; Vikash Rameshar

doi:10.1109/ICDABI53623.2021.9655783

Heart Disease Prediction using Machine Learning Techniques

Reldean Williams
, Thokozani Shongwe
, Ali N. Hasan
, Vikash Rameshar

Electrical and Electronic Engineering Technology

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

23 Citations (Scopus)

Abstract

One of the main contributors to death cases globally is heart diseases. Heart illnesses have an impact on many people in the middle or elderly age which, in most instances, lead to serious health adverse effects such as strokes and heart attacks. Therefore, it is necessary to diagnose and predict heart diseases to prevent any serious health issues before they occur. In this paper, a provisional study and examination, using different state of the art Machine Learning Techniques namely Artificial Neural Networks, Decision Trees and Naïve Bayes, Random Forest, Logistic Regression, Support Vector Machines and XG Boost, were implemented at various evaluation stages to predict heart diseases. Results show that Random Forest technique has outperformed the other techniques and achieved a prediction accuracy of 95%.

Original language	English
Title of host publication	2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	118-123
Number of pages	6
ISBN (Electronic)	9781665416566
DOIs	https://doi.org/10.1109/ICDABI53623.2021.9655783
Publication status	Published - 2021
Event	2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021 - Virtual, Online, Bahrain Duration: 25 Oct 2021 → 26 Oct 2021

Publication series

Name	2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

Conference

Conference	2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021
Country/Territory	Bahrain
City	Virtual, Online
Period	25/10/21 → 26/10/21

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Decision Trees
Heart Disease
Machine Learning
Naive Bayes
Neural Networks

ASJC Scopus subject areas

Business, Management and Accounting (miscellaneous)
Artificial Intelligence
Computer Networks and Communications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality
Health Informatics

Access to Document

10.1109/ICDABI53623.2021.9655783

Cite this

Williams, R., Shongwe, T., Hasan, A. N., & Rameshar, V. (2021). Heart Disease Prediction using Machine Learning Techniques. In 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021 (pp. 118-123). (2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDABI53623.2021.9655783

Williams, Reldean ; Shongwe, Thokozani ; Hasan, Ali N. et al. / Heart Disease Prediction using Machine Learning Techniques. 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 118-123 (2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021).

@inproceedings{d7818d312610420e858a6ddee8223954,

title = "Heart Disease Prediction using Machine Learning Techniques",

abstract = "One of the main contributors to death cases globally is heart diseases. Heart illnesses have an impact on many people in the middle or elderly age which, in most instances, lead to serious health adverse effects such as strokes and heart attacks. Therefore, it is necessary to diagnose and predict heart diseases to prevent any serious health issues before they occur. In this paper, a provisional study and examination, using different state of the art Machine Learning Techniques namely Artificial Neural Networks, Decision Trees and Na{\"i}ve Bayes, Random Forest, Logistic Regression, Support Vector Machines and XG Boost, were implemented at various evaluation stages to predict heart diseases. Results show that Random Forest technique has outperformed the other techniques and achieved a prediction accuracy of 95\%.",

keywords = "Decision Trees, Heart Disease, Machine Learning, Naive Bayes, Neural Networks",

author = "Reldean Williams and Thokozani Shongwe and Hasan, \{Ali N.\} and Vikash Rameshar",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021 ; Conference date: 25-10-2021 Through 26-10-2021",

year = "2021",

doi = "10.1109/ICDABI53623.2021.9655783",

language = "English",

series = "2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021",

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

pages = "118--123",

booktitle = "2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021",

address = "United States",

}

Williams, R, Shongwe, T, Hasan, AN & Rameshar, V 2021, Heart Disease Prediction using Machine Learning Techniques. in 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021. 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021, Institute of Electrical and Electronics Engineers Inc., pp. 118-123, 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021, Virtual, Online, Bahrain, 25/10/21. https://doi.org/10.1109/ICDABI53623.2021.9655783

Heart Disease Prediction using Machine Learning Techniques. / Williams, Reldean; Shongwe, Thokozani; Hasan, Ali N. et al.
2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 118-123 (2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021).

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

TY - GEN

T1 - Heart Disease Prediction using Machine Learning Techniques

AU - Williams, Reldean

AU - Shongwe, Thokozani

AU - Hasan, Ali N.

AU - Rameshar, Vikash

PY - 2021

Y1 - 2021

N2 - One of the main contributors to death cases globally is heart diseases. Heart illnesses have an impact on many people in the middle or elderly age which, in most instances, lead to serious health adverse effects such as strokes and heart attacks. Therefore, it is necessary to diagnose and predict heart diseases to prevent any serious health issues before they occur. In this paper, a provisional study and examination, using different state of the art Machine Learning Techniques namely Artificial Neural Networks, Decision Trees and Naïve Bayes, Random Forest, Logistic Regression, Support Vector Machines and XG Boost, were implemented at various evaluation stages to predict heart diseases. Results show that Random Forest technique has outperformed the other techniques and achieved a prediction accuracy of 95%.

AB - One of the main contributors to death cases globally is heart diseases. Heart illnesses have an impact on many people in the middle or elderly age which, in most instances, lead to serious health adverse effects such as strokes and heart attacks. Therefore, it is necessary to diagnose and predict heart diseases to prevent any serious health issues before they occur. In this paper, a provisional study and examination, using different state of the art Machine Learning Techniques namely Artificial Neural Networks, Decision Trees and Naïve Bayes, Random Forest, Logistic Regression, Support Vector Machines and XG Boost, were implemented at various evaluation stages to predict heart diseases. Results show that Random Forest technique has outperformed the other techniques and achieved a prediction accuracy of 95%.

KW - Decision Trees

KW - Heart Disease

KW - Machine Learning

KW - Naive Bayes

KW - Neural Networks

UR - https://www.scopus.com/pages/publications/85124651655

U2 - 10.1109/ICDABI53623.2021.9655783

DO - 10.1109/ICDABI53623.2021.9655783

M3 - Conference contribution

AN - SCOPUS:85124651655

T3 - 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

SP - 118

EP - 123

BT - 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

Y2 - 25 October 2021 through 26 October 2021

ER -

Williams R, Shongwe T, Hasan AN, Rameshar V. Heart Disease Prediction using Machine Learning Techniques. In 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 118-123. (2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021). doi: 10.1109/ICDABI53623.2021.9655783

Heart Disease Prediction using Machine Learning Techniques

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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