Iryna Perova

Work place: Kharkiv National University of Radio Electronics, Kharkiv, 61166, Ukraine



Research Interests: Computer systems and computational processes, Data Mining, Data Structures and Algorithms


Iryna Perova graduated from Kharkiv National University of Radio Electronics in 2004. She got her PhD in 2008. She obtained an academic title of the Senior Researcher in 2015. She obtained an academic title of the Associate Professor in 2016. Ph.D. Perova has been the associate professor in Biomedical engineering department at Kharkiv National University of Radio Electronics. She has more than 40 scientific publications including one invention. Her research interests are medical data mining, systems of computational intelligence, neuro- and neo-fuzzy-systems for medical diagnostics tasks, on-line systems that have to do with control, identification, clustering, diagnostics and fault detection.

Author Articles
Deep Hybrid System of Computational Intelligence with Architecture Adaptation for Medical Fuzzy Diagnostics

By Iryna Perova Iryna Pliss

DOI:, Pub. Date: 8 Jul. 2017

In the paper the deep hybrid system of computational intelligence with architecture adaptation for medical fuzzy diagnostics is proposed. This system allows to increase a quality of medical information processing under the condition of overlapping classes due to special adaptive architecture and training algorithms. The deep hybrid system under consideration can tune its architecture in situation when number of features and diagnoses can be variable. The special algorithms for its training are developed and optimized for situation of different system architectures without retraining of synaptic weights that have been tuned at previous steps. The proposed system was used for processing of three medical data sets (dermatology dataset, Pima Indians diabetes dataset and Parkinson disease dataset) under the condition of fixed number of features and diagnoses and in situation of its increasing. A number of conducted experiments have shown high quality of medical diagnostic process and confirmed the efficiency of the deep hybrid system of computational intelligence with architecture adaptation for medical fuzzy diagnostics.

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