The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the\ndiagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were\nstudied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following\nmachine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector\nmachine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature\nextraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation\nresults show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated\nfeature set search achieved higher results than data set based on the principal components.
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