Genetic programming application for features selection in task of arterial hypertension classification

Abstract

The paper investigates the possibilities of the genetic programming approach in task of arterial hypertension patients diagnosing. For this purpose, the 3-stage functional clinical study involving the tilt test was performed on two groups: relatively healthy volunteers and patients suffering from the arterial hypertension of II-III degree. The study was focused on the analysis of the 64 features of heart rate variability signals, evaluated by the time-domain, frequency-domain (Fourier and wavelet) and nonlinear methods. Performance of different machine learning approaches was compared: Discriminant Analysis, Nearest Neighbors, Decision Trees and Naive Bayes. All calculations were performed in the in-house software written on Python. The results of genetic programming application show the significant improvement of the classification accuracy over the previously obtained results of search on the non-correlated features space.