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

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

7 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

Keywords

Artificial intelligence
Biomedical
Healthcare
Heart disease
Machine learning

UN SDGs

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

Access to Document

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

Cite this

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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title = "Early Stage Detection of Heart Failure Using Machine Learning Techniques",

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.",

keywords = "Artificial intelligence, Biomedical, Healthcare, Heart disease, Machine learning",

author = "Zulfikar Alom and Azim, {Mohammad Abdul} and Zeyar Aung and Matloob Khushi and Josip Car and Moni, {Mohammad Ali}",

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doi = "10.1007/978-981-16-6636-0_7",

language = "British English",

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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

TY - CHAP

T1 - Early Stage Detection of Heart Failure Using Machine Learning Techniques

AU - Alom, Zulfikar

AU - Azim, Mohammad Abdul

AU - Aung, Zeyar

AU - Khushi, Matloob

AU - Car, Josip

AU - Moni, Mohammad Ali

PY - 2022

Y1 - 2022

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AB - 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.

KW - Artificial intelligence

KW - Biomedical

KW - Healthcare

KW - Heart disease

KW - Machine learning

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

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AN - SCOPUS:85120898103

T3 - Lecture Notes on Data Engineering and Communications Technologies

SP - 75

EP - 88

BT - Lecture Notes on Data Engineering and Communications Technologies

PB - Springer Science and Business Media Deutschland GmbH

ER -

Early Stage Detection of Heart Failure Using Machine Learning Techniques

Abstract

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Keywords

UN SDGs

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