Feature Selection for Differential Diagnosis of Asthma and COPD: A Preliminary Study

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dc.contributor.authorSargin, Serhat Ismet
dc.contributor.authorSen, Ipek
dc.date.accessioned2024-07-18T20:47:27Z
dc.date.available2024-07-18T20:47:27Z
dc.date.issued2023
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description31st IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUL 05-08, 2023 -- Istanbul Tech Univ, Ayazaga Campus, Istanbul, TURKEYen_US
dc.description.abstractAsthma and chronic obstructive pulmonary disease (COPD) are two obstructive pulmonary diseases whose differential diagnosis is difficult due to overlapping symptoms and inadequacy of classical methods. The main aim of this study is to understand which combination of acoustic properties is more discriminative, and to develop a new method that can be used in clinical practice. Accordingly, a total of 26 features under eight different feature types have been calculated using pulmonary sounds acquired from 50 volunteers diagnosed with asthma (30 subjects) and 20 copd (20 subjects), and forward sequential feature selection has been performed using k-nearest neighbor (k-NN) classifier as it was observed to have better performance than Bayesian classifier on the singular features extracted. Consequently, the feature set composed of F-max, BIN5, AR(3), P-max, and F-95 has been selected to be the best feature set by the k-NN classifier with 96% accuracy in asthma and COPD discrimination. To develop a more reliable method for the differential diagnosis of asthma and COPD, the feature set should be augmented and different types of classifiers should also be used.en_US
dc.description.sponsorshipIEEE,TUBITAK BILGEM,Turkcellen_US
dc.identifier.doi10.1109/SIU59756.2023.10223830
dc.identifier.isbn979-8-3503-4355-7
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85173523828en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/SIU59756.2023.10223830
dc.identifier.urihttps://hdl.handle.net/11411/7785
dc.identifier.wosWOS:001062571000077en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIEEEen_US
dc.relation.ispartof2023 31st Signal Processing and Communications Applications Conference, Siuen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPulmonary Sounds Analysisen_US
dc.subjectFeature Selectionen_US
dc.subjectDiagnostic Classificationen_US
dc.subjectK-Nearest Neighbor Classifieren_US
dc.subjectBayesian Classifieren_US
dc.subjectObstructive Pulmonary-Diseaseen_US
dc.titleFeature Selection for Differential Diagnosis of Asthma and COPD: A Preliminary Study
dc.typeConference Object

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