Robust Detection of Cardiac Disease Using Machine Learning Algorithms

Anas Domyati, Qurban A. Memon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

The contribution of the current work is to facilitate diagnose the heart disease based on contemporary machine learning algorithms. The performances of the classifiers are tested on feature spaces selected through various feature selection algorithms. The relief feature selection algorithm was selected for vital and more correlated features. The models were trained and tested on the Cleveland (S1) and Hungarian (S2) heart disease datasets. Several performance measures such as accuracy, sensitivity, specificity, and F1 score are used to observe the effectiveness of the selected models. It is found out that SVM and random forest achieved very promising results with both full feature space and selected feature space, specifically with relief feature selection algorithm.

Original languageEnglish
Title of host publicationICCCV 2022 - Proceedings of the 5th International Conference on Control and Computer Vision
PublisherAssociation for Computing Machinery
Pages52-55
Number of pages4
ISBN (Electronic)9781450397315
DOIs
Publication statusPublished - Aug 19 2022
Event5th International Conference on Control and Computer Vision, ICCCV 2022 - Virtual, Online, China
Duration: Aug 19 2022Aug 21 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Control and Computer Vision, ICCCV 2022
Country/TerritoryChina
CityVirtual, Online
Period8/19/228/21/22

Keywords

  • Random Forest
  • Relief feature selection
  • Robust detection
  • Support vector machine

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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