Alina S. Nechyporenko

Work place: Kharkiv National University of Radioelectronics, Biomedical Engineering Department,Software Engineering Department Kharkiv, 61166, Ukraine

E-mail: alina.nechyporenko@nure.ua

Website:

Research Interests: Analysis of Algorithms, Data Structures and Algorithms, Medical Image Computing, Swarm Intelligence, Artificial Intelligence, Medical Informatics

Biography

Alina S. Nechyporenko was born in 1979. She received the M.Sс. and Ph.D. degree in biomedical engineering from Kharkiv National University of Radiolectronics, Kharkiv, Ukraine in 2001, and 2009 respectively. Alina S. Nechyporenko is a member of IEEE, Engineering in Medicine and Biology Society (EMBS). She is currently an Associate Professor in the Department of Biomedical Engineering and Doctoral Student in Software Engineering Department at Kharkiv National University of Radioelectronics, Kharkiv, Ukraine. Her research interests include biomedical signal processing, implementation of computational intelligence methods for medical data analysis. She is a member of international research team which investigate nasal breathing disorders.

Author Articles
New Intelligent-based Approach for the Early Detection of Disorders: Use on Rhinological Data

By Alina S. Nechyporenko

DOI: https://doi.org/10.5815/ijigsp.2017.08.01, Pub. Date: 8 Aug. 2017

Medical data are characterized by complexity, inaccuracy, heterogeneity, the presence of hidden dependencies, often their distributions are unknown. Correlations between factors of disorders, including clinical data, parameters of time series, patient’s subjective assessments have a high complexity that cannot be fully comprehended by humans anymore. This problem is extremely important especially in case of the early detection of disorders. Machine learning methods are very useful for such detection task. Special area of interest is a problem of breathing disorders. In the paper, author demonstrates the potential use of computational intelligence tools for rhinologic data processing. Implementation of supervised learning techniques will allow improving accuracy of disorders detection as well as decrease medical insurance company expenses. Proposed intelligent-based approach makes it possible to process a variety of heterogeneous data in the medical domain. A combination of conventional and fractal features for time series of rhinomanometric data as well as inclusion of hydrodynamic characteristics of nasal breathing process provides the best accuracy. Such approach may be modified for other breathing disorders detection.

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