Early Stage Detection of Heart Failure Using Machine Learning Techniques

Zulfikar Alom, Mohammad Abdul Azim, Zeyar Aung, Matloob Khushi, Josip Car, Mohammad Ali Moni

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Scopus citations

Abstract

With a devastating health impact, heart attack prediction is an essential aspect of human health due to well understood early heart attack symptoms. The recent advancement of Artificial Intelligence (AI) and Machine learning (ML) provides a significant part in illness detection as well as prediction upon many phenomena. This makes AI and ML great techniques to predict heart attack prediction. This research chose the well-known Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) algorithms to predict heart attacks. A comparative study of the algorithmic performances is performed to identify the best algorithm that could be useful in the clinical decisions system.

Original languageBritish English
Title of host publicationLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Pages75-88
Number of pages14
DOIs
StatePublished - 2022

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
Volume95
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520

Keywords

  • Artificial intelligence
  • Biomedical
  • Healthcare
  • Heart disease
  • Machine learning

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