Early Stage Detection of Heart Failure Using Machine Learning Techniques

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

doi:10.1007/978-981-16-6636-0_7

Early Stage Detection of Heart Failure Using Machine Learning Techniques

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

Electrical Engineering

Asian University for Women
University of Sydney
University of Suffolk
Nanyang Technological University
Imperial College London
The University of Queensland

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

9 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 language	British English
Title of host publication	Lecture Notes on Data Engineering and Communications Technologies
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	75-88
Number of pages	14
DOIs	https://doi.org/10.1007/978-981-16-6636-0_7
State	Published - 2022

Publication series

Name	Lecture Notes on Data Engineering and Communications Technologies
Volume	95
ISSN (Print)	2367-4512
ISSN (Electronic)	2367-4520

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Artificial intelligence
Biomedical
Healthcare
Heart disease
Machine learning

Access to Document

10.1007/978-981-16-6636-0_7

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Alom, Z., Azim, M. A., Aung, Z., Khushi, M., Car, J., & Moni, M. A. (2022). Early Stage Detection of Heart Failure Using Machine Learning Techniques. In Lecture Notes on Data Engineering and Communications Technologies (pp. 75-88). (Lecture Notes on Data Engineering and Communications Technologies; Vol. 95). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-6636-0_7

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Alom, Z, Azim, MA, Aung, Z, Khushi, M, Car, J & Moni, MA 2022, Early Stage Detection of Heart Failure Using Machine Learning Techniques. in Lecture Notes on Data Engineering and Communications Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol. 95, Springer Science and Business Media Deutschland GmbH, pp. 75-88. https://doi.org/10.1007/978-981-16-6636-0_7

Early Stage Detection of Heart Failure Using Machine Learning Techniques. / Alom, Zulfikar; Azim, Mohammad Abdul; Aung, Zeyar et al.
Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH, 2022. p. 75-88 (Lecture Notes on Data Engineering and Communications Technologies; Vol. 95).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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KW - Machine learning

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ER -

Early Stage Detection of Heart Failure Using Machine Learning Techniques

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UN SDGs

Keywords

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