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Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics
Joint Authors
Kublanov, Vladimir S.
Dolganov, Anton Yu.
Belo, David
Gamboa, Hugo
Source
Applied Bionics and Biomechanics
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-31
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The 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.
American Psychological Association (APA)
Kublanov, Vladimir S.& Dolganov, Anton Yu.& Belo, David& Gamboa, Hugo. 2017. Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics. Applied Bionics and Biomechanics،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1121090
Modern Language Association (MLA)
Kublanov, Vladimir S.…[et al.]. Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics. Applied Bionics and Biomechanics No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1121090
American Medical Association (AMA)
Kublanov, Vladimir S.& Dolganov, Anton Yu.& Belo, David& Gamboa, Hugo. Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics. Applied Bionics and Biomechanics. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1121090
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1121090