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Comparison of machine learning methods for the arterial hypertension diagnostics

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dc.contributor.authorKublanov, Vladimir S.
dc.contributor.authorDolganov, Anton Yu
dc.contributor.authorBelo, David
dc.contributor.authorGamboa, Hugo
dc.contributor.institutionLIBPhys-UNL
dc.contributor.institutionDF – Departamento de Física
dc.contributor.institutionCeFITec – Centro de Física e Investigação Tecnológica
dc.contributor.pblIOS Press
dc.date.accessioned2018-11-30T23:25:27Z
dc.date.available2018-11-30T23:25:27Z
dc.date.issued2017
dc.descriptionAct 211 Government of the Russian Federation (02.A03.21.0006) FCT (AHA CMUP-ERI/HCI/0046/2013)
dc.description.abstractThe paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent3944570
dc.identifier.doi10.1155/2017/5985479
dc.identifier.issn1176-2322
dc.identifier.otherPURE: 3794256
dc.identifier.otherPURE UUID: feebbc0a-d641-4955-b1a8-644ba657e3e2
dc.identifier.otherScopus: 85031328575
dc.identifier.otherWOS: 000407088900001
dc.identifier.otherORCID: /0000-0002-4022-7424/work/43278999
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=85031328575&partnerID=8YFLogxK
dc.identifier.urlhttps://www.scopus.com/pages/publications/85031328575
dc.language.isoeng
dc.peerreviewedyes
dc.subjectEMPIRICAL MODE DECOMPOSITION
dc.subjectOBSTRUCTIVE SLEEP-APNEA
dc.subjectHEART-RATE-VARIABILITY
dc.subjectECG
dc.subjectDYNAMICS
dc.subjectSYSTEM
dc.subjectHEALTH
dc.subjectBiotechnology
dc.subjectMedicine (miscellaneous)
dc.subjectBioengineering
dc.subjectBiomedical Engineering
dc.titleComparison of machine learning methods for the arterial hypertension diagnosticsen
dc.typejournal article
degois.publication.titleApplied Bionics and Biomechanics
degois.publication.volume2017
dspace.entity.typePublication
rcaap.rightsopenAccess

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