FAST MEDICAL DIAGNOSTICS USING AUTOASSOCIATIVE NEURO-FUZZY MEMORY
DOI:
https://doi.org/10.47839/ijc.16.1.869Keywords:
medical diagnostics, medical data mining, computational intelligence, kernel fuzzy basis function, autoassociative neuro-fuzzy memory, neuro-fuzzy system.Abstract
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.References
R. Rizzo, “Computational intelligence methods for bioinformatics and biostatistics,” in Lecture Notes in Bioinformatics (7th International Meeting, CIBIB’2010), Palermo, Italy, September 16-18, 2010, Springer, 2011, 301 p.
A.N. Michel, J.A. Farrel, “Associative memories via artificial neural networks,” IEEE Control System Magazine, Vol. 10, Issue 3, pp. 6-17, 1990.
K.-L. Du, M.N.S. Swami, Neural Networks and Statistical Learning, London: Springer-Verlag, 2014, 824 p.
D.F. Specht, “Probabilistic neural networks for classification, mapping, or associative memory,” in Proceedings of the IEEE International Conference on Neural Networks, 1988, Vol. 1, pp. 525-432.
T. Hastie, R. Tibshirani, J. Friedman, Data Mining, Inference and Prediction, Springer-Verlag, 2009.
S. Haykin, Neural Networks and Learning Machine, New York: Prentice Hall, Inc., 2009, 906 p.
M.H. Hassoun, P.B. Watta, Associative Memory Networks, in “Handbook of Neural Computation” Oxford: IOP Publishing Ltd. and Oxford University Press, 1997.
D.A. Simovici, C. Djeraba, Mathematical Tools for Data Mining. Set Theory, Partial Orders, Combinatorics, Springer-Verlag, 2014.
J. Kacpzyk, W. Pedrycz, Handbook of Computational Intelligence, Springer-Verlag, 2015.
P.P. Angelov, Handbook on Computational Intelligence, World Scientific Publishing Company Pte Limited, 2016.
V. Torra, A. Dahlbom, Y. Narukawa, Fuzzy Sets, Rough Sets, Multisets and Clustering, Springer-Verlag, 2017.
K.J. Cios, W. Pedrycz, Neuro-fuzzy algorithms, In “Handbook on Neural Computation”. Oxford: IOP Publishing Ltd and Oxford University Press, 1997.
A. Casey, Soft Computing. Developments, Methods and Applications, Nova Science Publisher Inc., 2016.
L. Rutkovski, Flexible Neuro-Fuzzy Systems. Structure, Learning and Performance Evaluation, Kluwer Academic Publishers, Boston, 2004.
Z. Zeng, J. Wang, Advanced in Neural Network Research and Applications, Springer-Verlag, 2010.
I.N. Da Silva, D.H. Sparti, R.A. Flauzino, L.H. Batocci Liboni, S.F. dos Reis Alves, Artificial Neuron Networks. A Practical Course, Springer, 2017.
Ye. Bodyanskiy, N. Teslenko, “Autoassociative neural memory based on fuzzy basis functions,” Information Technology and Management Science, No. 44, pp. 9-14, 2010.
F. Höppner, F. Klawonn, R. Kruse, Fuzzy-Clusteranalyse: Verfahren für die Bilderkennung, Klassifikation und Datenanalyse, Braunschweig: Vieweg, 1996, 280 p.
P.M. Murphy, D. Aha, UCI Repository of machine learning databases. [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html.
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