Medical diagnosis using fuzzy cognitive map classifier

• Autorzy: Froelich W. , Wrobel K.
• Języki publikacji: EN

Abstrakty

EN

In this study, we address the problem of medical diagnosis by applying Fuzzy Cognitive Map (FCM). A distinctive feature of the FCM is its ability to simulate the development of the disease in time. By this simulation, it is possible to predict the severity of the disease by having future knowledge on current medical investigations. For the first time in this paper, we construct an FCM-based classifier dedicated solely to perform medical diagnosis. To learn the FCM, we use an evolutionary algorithm explicitly specifying the newly designed fitness function. Real, publicly available medical data are applied for the validation and evaluation of the proposed approach.

Słowa kluczowe

EN

fuzzy cognitive map; medical diagnosis; classification

PL

rozmyta mapa poznawcza; diagnostyka medyczna; klasyfikacja

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2015
• Tom: Vol. 24
• Strony: 247--254
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Froelich W.

• Institute of Computer Science, University of Silesia, ul. Bedzinska 39, Sosnowiec, Poland

autor

Wrobel K.

• Institute of Computer Science, University of Silesia, ul. Bedzinska 39, Sosnowiec, Poland

Bibliografia

• [1] ALIZADEH S., GHAZANFARI M. Learning fcm by chaotic simulated annealing. Chaos, Solitons & Fractals, 2009, Vol. 41. pp. 1182–1190.
• [2] BERTHOLD M. R. Mixed fuzzy rule formation. International Journal of Approximate Reasoning, 2003, Vol. 32. pp. 67– 84.
• [3] FOELICH W., SALMERON J. L. Evolutionary learning of fuzzy grey cognitive maps for the forecasting of multivariate, interval-valued time series. International Journal of Approximate Reasoning, 2014, Vol. 55. pp. 1319–1335.
• [4] FROELICH W., JUSZCZUK P. Predictive capabilities of adaptive and evolutionary fuzzy cognitive maps - a comparative study. Intelligent Systems for Knowledge Management, 2009, Vol. 252 of Studies in Computational Intelligence. Springer, pp. 153–174.
• [5] FROELICH W., PAPAGEORGIOU E. Extended evolutionary learning of fuzzy cognitive maps for the prediction of multivariate time-series. Fuzzy Cognitive Maps for Applied Sciences and Engineering, 2014. Springer Berlin Heidelberg, pp. 121–131.
• [6] FROELICH W., WAKULICZ-DEJA A. Associational cognitive maps for medical diagnosis support. Proceedings of the International Intelligent Information Systems Conference, 2008. pp. 387–96.
• [7] GOLDBERG D. Genetic algorithms. 2013. Pearson Publishing.
• [8] HUERGA A. V. A balanced differential learning algorithm in fuzzy cognitive maps. Proceedings of the 16th International Workshop on Qualitative Reasoning, 2002. pp. 1–7.
• [9] JOHN G. H., LANGLEY P. Estimating continuous distributions in bayesian classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 1995, UAI’95. Morgan Kaufmann Publishers Inc., pp. 338–345.
• [10] JUSZCZUK P., FROELICH W. Learning fuzzy cognitive maps using a differential evolution algorithm. Polish Journal of Environmental Studies, 2009, Vol. 12. pp. 108–112.
• [11] KANNAPPAN A., TAMILARASI A., PAPAGEORGIOU E. I. Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder. Expert Systems with Applications, 2011, Vol. 38. pp. 1282–1292.
• [12] KOSKO B. Differential hebbian learning. Neural Networks for Computing, American Institute of Physics, 1986, Vol. April. pp. 277–282.
• [13] LICHMAN M. UCI machine learning repository. 2013.
• [14] PAPAGEORGIOU E., STYLIOS C. D., GROUMPOS P. P. Active hebbian learning algorithm to train fuzzy cognitive maps. International Journal of Approximate Reasoning, 2004, Vol. 37. pp. 219–249.
• [15] PAPAGEORGIOU E.I., KANNAPPAN A. Fuzzy cognitive map ensemble learning paradigm to solve classification problems: Application to autism identification. Applied Soft Computing, 2012, Vol. 12. pp. 3798–3809.
• [16] PAPAGEORGIOU E. I., PARSOPOULOS K. E., STYLIOS C. D., GROUMPOS P. P., VRAHATIS M. N. Fuzzy cognitive maps learning using particle swarm optimization. Journal of Intelligent Information Systems, 2005, Vol. 25. pp. 95–121.
• [17] PAPAGEORGIOU E. I., STYLIOS C. D., GROUMPOS P. P. Fuzzy cognitive map learning based on nonlinear hebbian rule. Australian Conference on Artificial Intelligence, 2003. pp. 256–268.
• [18] PAPAKOSTAS G. A., KOULOURIOTIS D. E., POLYDOROS A. S., TOURASSIS V. D. Towards hebbian learning of fuzzy cognitive maps in pattern classification problems. Expert Systems with Applications, 2012, Vol. 39. pp. 10620–10629.
• [19] SONG H., MIAO C., SHEN Z., ROEL W., MAJA D., FRANCKY C. Design of fuzzy cognitive maps using neural networks for predicting chaotic time series. Neural Networks, 2010, Vol. 23. pp. 1264–1275.
• [20] STACH W., KURGAN L., PEDRYCZ W., REFORMAT M. Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems, 2005, Vol. 153. pp. 371–401.
• [21] STACH W., KURGAN L. A., PEDRYCZ W. Numerical and linguistic prediction of time series with the use of fuzzy cognitive maps. IEEE T. Fuzzy Systems, 2008, Vol. 16. pp. 61–72.