A decision tree - Based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds

Pavlopoulos, SA; Stasis, ACH; Loukis, EN

dc.contributor.author	Pavlopoulos, SA	en
dc.contributor.author	Stasis, ACH	en
dc.contributor.author	Loukis, EN	en
dc.date.accessioned	2014-03-01T01:19:44Z
dc.date.available	2014-03-01T01:19:44Z
dc.date.issued	2004	en
dc.identifier.issn	1475925X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15684
dc.subject	Aortic Stenosis	en
dc.subject	Cardiology	en
dc.subject	Classification Accuracy	en
dc.subject	Differential Diagnosis	en
dc.subject	Generalization Capability	en
dc.subject	Heart Disease	en
dc.subject	Mitral Regurgitation	en
dc.subject	New Technology	en
dc.subject	Primary Healthcare	en
dc.subject	Rural Area	en
dc.subject	Support System	en
dc.subject	Training Program	en
dc.subject	Decision Support System	en
dc.subject	Decision Tree	en
dc.subject	Decision Tree Classifier	en
dc.subject	Internal Medicine	en
dc.subject	Not Significant	en
dc.subject	Rule Based	en
dc.subject.other	Aortic stenosis (AS)	en
dc.subject.other	Data sets	en
dc.subject.other	Heart sounds	en
dc.subject.other	Mitral regugitation (MR)	en
dc.subject.other	Data reduction	en
dc.subject.other	Decision support systems	en
dc.subject.other	Disease control	en
dc.subject.other	Doppler effect	en
dc.subject.other	Echocardiography	en
dc.subject.other	Health care	en
dc.subject.other	Magnetic resonance imaging	en
dc.subject.other	Trees (mathematics)	en
dc.subject.other	Waveform analysis	en
dc.subject.other	Cardiology	en
dc.subject.other	accuracy	en
dc.subject.other	aorta stenosis	en
dc.subject.other	article	en
dc.subject.other	controlled study	en
dc.subject.other	data analysis	en
dc.subject.other	decision support system	en
dc.subject.other	decision theory	en
dc.subject.other	diastole	en
dc.subject.other	differential diagnosis	en
dc.subject.other	disease classification	en
dc.subject.other	frequency analysis	en
dc.subject.other	heart sound	en
dc.subject.other	human	en
dc.subject.other	major clinical study	en
dc.subject.other	mathematical computing	en
dc.subject.other	medical record	en
dc.subject.other	mitral valve regurgitation	en
dc.subject.other	morphology	en
dc.subject.other	phonocardiography	en
dc.subject.other	signal processing	en
dc.subject.other	stethoscope	en
dc.subject.other	systolic heart murmur	en
dc.subject.other	waveform	en
dc.title	A decision tree - Based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds	en
heal.type	journalArticle	en
heal.identifier.primary	10.1186/1475-925X-3-21	en
heal.identifier.secondary	http://dx.doi.org/10.1186/1475-925X-3-21	en
heal.identifier.secondary	21	en
heal.publicationDate	2004	en
heal.abstract	Background: New technologies like echocardiography, color Doppler, CT, and MRI provide more direct and accurate evidence of heart disease than heart auscultation. However, these modalities are costly, large in size and operationally complex and therefore are not suitable for use in rural areas, in homecare and generally in primary healthcare set-ups. Furthermore the majority of internal medicine and cardiology training programs underestimate the value of cardiac auscultation and junior clinicians are not adequately trained in this field. Therefore efficient decision support systems would be very useful for supporting clinicians to make better heart sound diagnosis. In this study a rule-based method, based on decision trees, has been developed for differential diagnosis between ""clear"" Aortic Stenosis (AS) and ""clear"" Mitral Regurgitation (MR) using heart sounds. Methods: For the purposes of our experiment we used a collection of 84 heart sound signals including 41 heart sound signals with ""clear:AS systolic murmur and 43 with ""clear:MR systolic murmur. Signals were initially preprocessed to detect 1st and 2nd heart sounds. Next a total of 100 features were determined for every heart sound signal and relevance to the differentiation between AS and MR was estimated. The performance of fully expanded decision tree classifiers and Pruned decision tree classifiers were studied based on various training and test datasets. Similarly, pruned decision tree classifiers were used to examine their differentiation capabilities. In order to build a generalized decision support system for heart sound diagnosis, we have divided the problem into sub problems, dealing with either one morphological characteristic of the heart-sound waveform or with difficult to distinguish cases. Results: Relevance analysis on the different heart sound features demonstrated that the most relevant features are the frequency features and the morphological features that describe S1, S2 and the systolic murmur. The results are compatible with the physical understanding of the problem since AS and MR systolic murmurs have different frequency contents and different waveform shapes. On the contrary, in the diastolic phase there is no murmur in both diseases which results in the fact that the diastolic phase signals cannot contribute to the differentiation between AS and MR. We used a fully expanded decision tree classifier with a training set of 34 records and a test set of 50 records which resulted in a classification accuracy (total corrects/total tested) of 90% (45 correct/50 total records). Furthermore, the method proved to correctly classify both AS and MR cases since the partial AS and MR accuracies were 91.6% and 88.5% respectively. Similar accuracy was achieved using decision trees with a fraction of the 100 features (the most relevant). Pruned Differentiation decision trees did not significantly change the classification accuracy of the decision trees both in terms of partial classification and overall classification as well. Discussion: Present work has indicated that decision tree algorithms decision tree algorithms can be successfully used as a basis for a decision support system to assist young and inexperienced clinicians to make better heart sound diagnosis. Furthermore, Relevance Analysis can be used to determine a small critical subset, from the initial set of features, which contains most of the information required for the differentiation. Decision tree structures, if properly trained can increase their classification accuracy in new test data sets. The classification accuracy and the generalization capabilities of the Fully Expanded decision tree structures and the Pruned decision tree structures have not significant difference for this examined sub-problem. However, the generalization capabilities of the decision tree based methods were found to be satisfactory. Decision tree structures were tested on various training and test data set and the classification accuracy was found to be consistently high. © 2004 Pavlopoulos et al; licensee BioMed Central Ltd.	en
heal.journalName	BioMedical Engineering Online	en
dc.identifier.doi	10.1186/1475-925X-3-21	en
dc.identifier.volume	3	en