Review of Soft Computing Techniques in Monitoring Cardiovascular Disease in the Context of South Asian Countries

Thakur, GS; Sahu, SK; Swamy, NK; Gupta, M; Jan, T; Prasad, M

Review of Soft Computing Techniques in Monitoring Cardiovascular Disease in the Context of South Asian Countries

Thakur, GS Sahu, SK Swamy, NK Gupta, M Jan, T Prasad, M

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Applied Sciences (Switzerland), 2023, 13, (17)
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Thakur, GS
dc.contributor.author	Sahu, SK
dc.contributor.author	Swamy, NK
dc.contributor.author	Gupta, M
dc.contributor.author	Jan, T
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.date.accessioned	2024-05-03T02:33:44Z
dc.date.available	2024-05-03T02:33:44Z
dc.date.issued	2023-09-01
dc.identifier.citation	Applied Sciences (Switzerland), 2023, 13, (17)
dc.identifier.issn	2076-3417
dc.identifier.issn	2076-3417
dc.identifier.uri	http://hdl.handle.net/10453/178582
dc.description.abstract	The term “soft computing” refers to a system that can work with varying degrees of uncertainty and approximations in real-life complex problems using various techniques such as Fuzzy Logic, Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). Owing to the low-cost and high-performance digital processors today, the use of soft computing techniques has become more prevalent. The main focus of this paper is to study the use of soft computing in the prediction and diagnosis of heart diseases, which are considered one of the major causes of fatalities in modern-day humans. The heart is a major human organ that can be affected by various conditions such as high blood pressure, diabetes, and heart failure. The main cause of heart failure is the narrowing of the blood vessels due to excess cholesterol deposits in the coronary arteries. The objective of this study is to review and compare the various soft computing techniques that are used for the prediction, diagnosis, failure, detection, identification, and classification of heart disease. In this paper, a comprehensive list of recent soft computing techniques in heart condition monitoring is reviewed and compared with an experiment with specific applications to developing countries including South Asian countries. The relevant experimental outcomes demonstrate the benefits of soft computing in medical services with a high accuracy of 99.4% from Fuzzy Logic and Convolutional Neural Networks, with comparable results from other competing state-of-the-art soft computing models.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Applied Sciences (Switzerland)
dc.relation.isbasedon	10.3390/app13179555
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Review of Soft Computing Techniques in Monitoring Cardiovascular Disease in the Context of South Asian Countries
dc.type	Journal Article
utslib.citation.volume	13
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-05-03T02:33:42Z
pubs.issue	17
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	17

Abstract:

The term “soft computing” refers to a system that can work with varying degrees of uncertainty and approximations in real-life complex problems using various techniques such as Fuzzy Logic, Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). Owing to the low-cost and high-performance digital processors today, the use of soft computing techniques has become more prevalent. The main focus of this paper is to study the use of soft computing in the prediction and diagnosis of heart diseases, which are considered one of the major causes of fatalities in modern-day humans. The heart is a major human organ that can be affected by various conditions such as high blood pressure, diabetes, and heart failure. The main cause of heart failure is the narrowing of the blood vessels due to excess cholesterol deposits in the coronary arteries. The objective of this study is to review and compare the various soft computing techniques that are used for the prediction, diagnosis, failure, detection, identification, and classification of heart disease. In this paper, a comprehensive list of recent soft computing techniques in heart condition monitoring is reviewed and compared with an experiment with specific applications to developing countries including South Asian countries. The relevant experimental outcomes demonstrate the benefits of soft computing in medical services with a high accuracy of 99.4% from Fuzzy Logic and Convolutional Neural Networks, with comparable results from other competing state-of-the-art soft computing models.

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http://hdl.handle.net/10453/178582