Diagnostic Classification of Heart Disease Using Boosting Algorithms

Dolley Srivastava, Himanshu Pandey, Ambuj Kumar Agarwal, Richa Sharma

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

Abstract

Heart disease is rapidly overtaking other causes of death in India, and it is a major threat to both men and women. Among the top causes of mortality throughout the globe, heart disease ranks first. Therefore, it is crucial to accurately and rapidly forecast cardiac problems at an early stage to guarantee the lives of millions of people. In the realm of medicine, machine learning has become more important. The realm of machine learning is where the idea of boosting first appeared. The underlying premise is that by merging multiple cases into a more accurate forecast, the accuracy of a rather ineffective classifying tool may be improved. Subsequently, this overarching idea was implemented in statistical modeling. The purpose of this research is to evaluate and contrast the performance of four popular boosting algorithms for machine learning in the context of cardiac illness diagnosis and prediction. Test results show that among the models evaluated, XGBoost performed best in terms of classification accuracy.

Original languageEnglish
Title of host publicationProceedings of 4th Doctoral Symposium on Computational Intelligence - DoSCI 2023
EditorsAbhishek Swaroop, Vineet Kansal, Giancarlo Fortino, Aboul Ella Hassanien
PublisherSpringer Science and Business Media Deutschland GmbH
Pages501-508
Number of pages8
ISBN (Print)9789819937158
DOIs
StatePublished - 1 Jan 2023
Externally publishedYes
Event4th Doctoral Symposium on Computational Intelligence, DoSCI 2023 - Virtual, Online
Duration: 3 Mar 20233 Mar 2023

Publication series

NameLecture Notes in Networks and Systems
Volume726 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference4th Doctoral Symposium on Computational Intelligence, DoSCI 2023
CityVirtual, Online
Period3/03/233/03/23

Keywords

  • Explainable machine learning
  • Heart disease
  • Machine learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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