- author = "Vladimir Kublanov and Anton Dolganov and Hugo Gamboa",
- booktitle = "2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)",
- title = "Genetic programming application for features selection in task of arterial hypertension classification",
- year = "2017",
- pages = "561--565",
- 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 Neighbours, 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.",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/SIBIRCON.2017.8109954",
- month = sep,
-
notes = "Kublanov Vladimir and Dolganov Anton and Gamboa
Hugo
Also known as \cite{8109954}",
