The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2018.090533
PDF

Hybrid Ensemble Framework for Heart Disease Detection and Prediction

Author 1: Elham Nikookar
Author 2: Ebrahim Naderi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 5, 2018.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Data mining techniques have been widely used in clinical decision support systems for detection and prediction of various diseases. As heart disease is the leading cause of death for both men and women, detection and prediction of the heart disease is one of the most important issues in medical domain and many researchers developed intelligent medical decision support systems to improve the ability of the CAD systems in diagnosing heart disease. However, there are almost no studies investigating capabilities of hybrid ensemble methods in building a detection and prediction model for heart disease. In this work, we investigate the use of hybrid ensemble model in which a more reliable ensemble than basic ensemble models is proposed and leads to better performance than other heart disease prediction models. To evaluate the performance of proposed model, a dataset containing 278 samples from SPECT heart disease database is used that after applying the model on the data, 96% of classification accuracy, 80% of sensitivity and 93% of specificity are obtained that indicates acceptable performance of the proposed hybrid ensemble model in comparison with basic ensemble model as well as other state of the art models.

Keywords: Data mining; hybrid ensemble; base classifier; classification accuracy; sensitivity; specificity

Elham Nikookar and Ebrahim Naderi, “Hybrid Ensemble Framework for Heart Disease Detection and Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 9(5), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090533

@article{Nikookar2018,
title = {Hybrid Ensemble Framework for Heart Disease Detection and Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090533},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090533},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {5},
author = {Elham Nikookar and Ebrahim Naderi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org