• Iryna Perova
  • Yevgeniy Bodyanskiy



medical diagnostics, medical data mining, computational intelligence, kernel fuzzy basis function, autoassociative neuro-fuzzy memory, neuro-fuzzy system.


This paper proposes an architecture of fast medical diagnostics system based on autoassociative neuro-fuzzy memory. The architecture of proposed system is close to traditional Takagi-Sugeno-Kang neuro-fuzzy system, but it is based on other principles. This system contains of recording subsystem and pattern retrieval subsystem, where diagnostics of patients with unknown diagnoses is realized. Level of memberships for all other possible diagnoses from recording subsystem is determined too. System tuning is based on lazy learning procedure and “neurons in data points” principle and uses bell-shaped fuzzy basis functions. Number of these functions changes during training process using principles of evolving connectionist systems. Bell-shaped membership functions centers can be tuned using proposed algorithm, processes of accumulation patients in fundamental memory and patients retrieval are described. This hybrid neuro-fuzzy associative memory combines advantages of fuzzy inference systems, artificial neural networks and evolving systems and its using provides the increasing of autoassociative memories capacity without essential complication of its architecture for medical diagnostics tasks.


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How to Cite

Perova, I., & Bodyanskiy, Y. (2017). FAST MEDICAL DIAGNOSTICS USING AUTOASSOCIATIVE NEURO-FUZZY MEMORY. International Journal of Computing, 16(1), 34-40.