Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

Overview

Journal Appl Bionics Biomech

Publisher Wiley

Date 2017 Aug 24

PMID 28831239

Citations 12

Authors

Vladimir S Kublanov

Anton Yu Dolganov

David Belo

Hugo Gamboa

Affiliations

Soon will be listed here.

Abstract

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, -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.

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