Heart Disease Prediction using Machine Learning Techniques

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 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 languageEnglish
Title of host publication2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages118-123
Number of pages6
ISBN (Electronic)9781665416566
DOIs
Publication statusPublished - 2021
Event2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021 - Virtual, Online, Bahrain
Duration: 25 Oct 202126 Oct 2021

Publication series

Name2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

Conference

Conference2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021
Country/TerritoryBahrain
CityVirtual, Online
Period25/10/2126/10/21

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

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