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: Book chapter/Published conference paperChapter

6 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the international conference on big data, IoT, and machine learning
Subtitle of host publicationBIM 2021
EditorsMohammad Shamsul Arefin, M. Shamim Kaiser , Anirban Bandyopadhyay , Md. Atiqur Rahman Ahad , Kanad Ray
Place of PublicationSingapore
PublisherSpringer
Chapter7
Pages75-88
Number of pages14
Edition1st
ISBN (Electronic)9789811666360
ISBN (Print)9789811666353
DOIs
Publication statusPublished - 2022

Publication series

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

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