Cybernetics and Computer Engineering, 2019, 4(198)
PEROVA I.G., PhD (Engineering), Associate Professor
Associate Professor of Biomedical Engineering Department,
BODYANSKIY Ye.V., DSc (Engineering), Professor
Professor of Artificial Intelligence Department
Kharkiv National University of Radio Electronics,
14, Nauky av., Kharkiv, 61166, Ukraine.
ONLINE MEDICAL DATA STREAM MINING BASED ON ADAPTIVE NEURO-FUZZY APPROACHES
Introduction. Data mining approaches in medical diagnostics tasks have a number of special properties that do not allow the use of such approaches in a classical form. That’s why adaptive neuro-fuzzy systems for online medical data stream processing tasks and its learning algorithms have been developed. Proposed systems can process medical data streams in three modes: supervised learning, unsupervised learning and active learning.
The purpose of the paper is to develop approach, based on adaptive neuro-fuzzy systems to solve the tasks of medical data stream mining in online-mode.
Methods. The methods of computational intelligence are used for medical data stream processing and, first of all, artificial neural networks, neuro-fuzzy systems, neo-fuzzy systems, their supervised, unsupervised and active learning approaches, gradient methods of optimization, methods of evolving system.
Results. As a result, approbation of the developed approach in supervised learning mode using multidimensional neo-fuzzy neuron on medical data of patients with urological disease was investigated. Percentage of errors in system testing using all feature space is 11.11 %, using the most informative features the error rate becomes 6.4 %. Also multidimensional neo-fuzzy neuron was used for diagnostic of the pharmacoresistant form of epilepsy, percentage of errors in system testing is 5.82 %. Approval of the developed approach in the mode of active training and association on the data of patients with pulmonary diseases was performed. For all approbation results performance criterion was calculated, its values are suitable for the tasks of medical diagnostics in data stream mode.
Conclusions. The proposed neuro-fuzzy approaches allow obtaining additional information about patients’ diagnosis in conditions of limited a priori information about patient.
Keywords: adaptive system, neuro-fuzzy system, medical data mining, medical data stream.
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