A decision tree-based method, using auscultation findings, for the differential diagnosis of aortic stenosis from mitral regurgitation

Stasis, ACh; Loukis, EN; Pavlopoulos, SA; Koutsouris, D

dc.contributor.author	Stasis, ACh	en
dc.contributor.author	Loukis, EN	en
dc.contributor.author	Pavlopoulos, SA	en
dc.contributor.author	Koutsouris, D	en
dc.date.accessioned	2014-03-01T02:42:10Z
dc.date.available	2014-03-01T02:42:10Z
dc.date.issued	2003	en
dc.identifier.issn	02766574	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30839
dc.subject	Aortic Stenosis	en
dc.subject	Classification Accuracy	en
dc.subject	Data Mining	en
dc.subject	Differential Diagnosis	en
dc.subject	Mitral Regurgitation	en
dc.subject	Decision Tree	en
dc.subject	Decision Tree Classifier	en
dc.subject.other	Acoustic intensity	en
dc.subject.other	Algorithms	en
dc.subject.other	Blood	en
dc.subject.other	Cardiology	en
dc.subject.other	Computerized tomography	en
dc.subject.other	Database systems	en
dc.subject.other	Decision support systems	en
dc.subject.other	Diseases	en
dc.subject.other	Echocardiography	en
dc.subject.other	Health care	en
dc.subject.other	Magnetic resonance imaging	en
dc.subject.other	Spectrum analysis	en
dc.subject.other	Aortic stenosis (AS)	en
dc.subject.other	Decision tree structures	en
dc.subject.other	Heart sound signals	en
dc.subject.other	Mitral regurgitation (MR)	en
dc.subject.other	Diagnosis	en
dc.title	A decision tree-based method, using auscultation findings, for the differential diagnosis of aortic stenosis from mitral regurgitation	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/CIC.2003.1291270	en
heal.identifier.secondary	http://dx.doi.org/10.1109/CIC.2003.1291270	en
heal.publicationDate	2003	en
heal.abstract	In this study, Decision Tree algorithms are used with promising results in a crucial and at the same time complicated classification problem concerning differential diagnosis of heart sounds. Decision Tree structures are constructed, using data mining/distillation methods and then are used to classify heart sounds that were recorded from patients that have either Aortic Stenosis (AS) or Mitral Regurgitation (MR). Emphasis is given on the selection of the appropriate features that are adequately independent from the heart sound signal acquisition method. The differentiation capabilities and the classification performance of the fully expanded Decision Tree classifiers and the pruned Decision tree classifiers are studied for this problem. For each constructed Decision Tree classifier the partial classification accuracies for the AS and MR auscultation findings are also estimated.	en
heal.journalName	Computers in Cardiology	en
dc.identifier.doi	10.1109/CIC.2003.1291270	en
dc.identifier.volume	30	en
dc.identifier.spage	769	en
dc.identifier.epage	772	en