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A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms

Adaptive boosting; Biological organs; Decision trees; Pathology; Random forests; Ada boost classifiers; Boosting classifiers; Classification accuracy; Ensemble classifiers; Gradient boosting; Information exchanges; Random forest algorithm; Respiratory pathology; Telemedicine

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Main Authors:	Jaber M.M., Abd S.K., Shakeel P.M., Burhanuddin M.A., Mohammed M.A., Yussof S.
Other Authors:	56519461300
Format:	Article
Published:	Elsevier B.V. 2023
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id	my.uniten.dspace-25223
record_format	dspace
spelling	my.uniten.dspace-252232023-05-29T16:07:25Z A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms Jaber M.M. Abd S.K. Shakeel P.M. Burhanuddin M.A. Mohammed M.A. Yussof S. 56519461300 56516784600 57189758693 22733350000 55601160600 16023225600 Adaptive boosting; Biological organs; Decision trees; Pathology; Random forests; Ada boost classifiers; Boosting classifiers; Classification accuracy; Ensemble classifiers; Gradient boosting; Information exchanges; Random forest algorithm; Respiratory pathology; Telemedicine Telemedicine is one of the medical services related to information exchange tools (eHealth). In recent years, the monitoring and classification of acoustic signals of respiratory-related disease is a significant characteristic in the pulmonary analysis. Lung sounds produce appropriate evidence related to pulmonary disorders, and to assess subjects pulmonary situations. However, this traditional method suffers from restrictions, such as if the doctor isn't very much practiced, this may lead to an incorrect analysis. The objective of this research work is to build a telemedicine framework to predict respiratory pathology using lung sound examination. In this paper, the three approaches has been compared to machine learning for the detection of lung sounds. The proposed telemedicine framework trained through Bagging and Boosting classifiers (Improved Random Forest, AdaBoost, Gradient Boosting algorithm) with an extracted set of handcrafted features. The experimental results demonstrated that the performance of Improved Random Forest was higher than Gradient Boosting and AdaBoost classifiers. The overall classification accuracy for the Improved Random Forest algorithm has 99.04%. The telemedicine framework was implemented with the Improved Random Forest algorithm. The telemedicine framework has achieved phenomenal performance in recognizing respiratory pathology. � 2020 Elsevier Ltd Final 2023-05-29T08:07:25Z 2023-05-29T08:07:25Z 2020 Article 10.1016/j.measurement.2020.107883 2-s2.0-85084562911 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084562911&doi=10.1016%2fj.measurement.2020.107883&partnerID=40&md5=a0232e1ffe9adff67e46235233669b1f https://irepository.uniten.edu.my/handle/123456789/25223 162 107883 Elsevier B.V. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Adaptive boosting; Biological organs; Decision trees; Pathology; Random forests; Ada boost classifiers; Boosting classifiers; Classification accuracy; Ensemble classifiers; Gradient boosting; Information exchanges; Random forest algorithm; Respiratory pathology; Telemedicine
author2	56519461300
author_facet	56519461300 Jaber M.M. Abd S.K. Shakeel P.M. Burhanuddin M.A. Mohammed M.A. Yussof S.
format	Article
author	Jaber M.M. Abd S.K. Shakeel P.M. Burhanuddin M.A. Mohammed M.A. Yussof S.
spellingShingle	Jaber M.M. Abd S.K. Shakeel P.M. Burhanuddin M.A. Mohammed M.A. Yussof S. A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms
author_sort	Jaber M.M.
title	A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms
title_short	A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms
title_full	A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms
title_fullStr	A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms
title_full_unstemmed	A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms
title_sort	telemedicine tool framework for lung sounds classification using ensemble classifier algorithms
publisher	Elsevier B.V.
publishDate	2023
_version_	1806427346566119424
score	13.211869

A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms

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