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

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dc.citedby10
dc.contributor.authorJaber M.M.en_US
dc.contributor.authorAbd S.K.en_US
dc.contributor.authorShakeel P.M.en_US
dc.contributor.authorBurhanuddin M.A.en_US
dc.contributor.authorMohammed M.A.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorid56519461300en_US
dc.contributor.authorid56516784600en_US
dc.contributor.authorid57189758693en_US
dc.contributor.authorid22733350000en_US
dc.contributor.authorid55601160600en_US
dc.contributor.authorid16023225600en_US
dc.date.accessioned2023-05-29T08:07:25Z
dc.date.available2023-05-29T08:07:25Z
dc.date.issued2020
dc.descriptionAdaptive 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; Telemedicineen_US
dc.description.abstractTelemedicine 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 Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo107883
dc.identifier.doi10.1016/j.measurement.2020.107883
dc.identifier.scopus2-s2.0-85084562911
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85084562911&doi=10.1016%2fj.measurement.2020.107883&partnerID=40&md5=a0232e1ffe9adff67e46235233669b1f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25223
dc.identifier.volume162
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleMeasurement: Journal of the International Measurement Confederation
dc.titleA telemedicine tool framework for lung sounds classification using ensemble classifier algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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