Fuzzy neuro genetic approach for predicting the risk of cardiovascular diseases

International Journal of Data Mining, Modelling and Management

Volumn 2, Issue 4, 2010, Pages 388-402

  Vijaya, Kalavakonda ^a   Nehemiah, H Khanna ^a   Kannan, A ^a   Bhuvaneswari, N G ^b  

^a ANNA UNIVERSITY   (India)

^b NOORUL ISLAM UNIVERSITY   (India)

Author keywords

Back propagation algorithm; Cardiovascular disease; Classification; CVD; Fuzzy inference; Fuzzy logic; Genetic algorithm; Knowledge base; Medical diagnosis; Neural network; NN

Indexed keywords

EID: 84858815512     PISSN: 17591163     EISSN: 17591171     Source Type: Journal    
DOI: 10.1504/IJDMMM.2010.035565     Document Type: Article

References (35)

1. Aeinfar, V., Mazdarani, H., Deregeh, F., Hayati, M. and Payandeh, M. (2009) 'Multilayer peceptron neural network with supervised training method for diagnosis and predicting blood disorder and cancer', IEEE International Symposium on Industrial Electronics, 5-8 July, Seoul, Korea, pp. 2075-2080.
2. Allalverdi, N. Torun, S. and Saritas, I. (2007) 'Design of a fuzzy expert system for determination of coronary heart disease risk', International Conference on Computer Systems and Technologies, pp. IIIA.14-1-14-8.
3. Alty, S.R., Millasseau, S.C., Chowienczyk, P.J. and Jakobsson, A. (2004) 'Cardiovascular disease prediction using support vector machines', Proceedings of 46th IEEE International Midwest Symposium, pp. 376-379.
4. Baxt, W.G. (1990) 'Use of an artificial neural network for data analysis in clinical decision-making: the diagnosis of acute coronary occlusion', Neural Computation, Vol. 2, No. 4, pp. 480-489.
5. Dam, H.H., Abbass, H.A. and Yao, X. (2008) 'Neural - based learning classifier systems', IEEE Transactions on Knowledge and Data Engineering, Vol. 20, No. 1, pp. 26-39.
6. Eom, J-H., Kim, S-C. and Zhang, B-T. (2008) 'AptaCDSS-E: a classifier ensemble-based clinical decision support system for cardiovascular disease level prediction', International Journal on Expert Systems with Applications, Vol. 34, No. 4, pp. 2465-2479. (Pubitemid 351181386)
7. Goldberg, D.E. (2006) Genetic Algorithms in Search, Optimization & Machine Learning, Pearson Education Inc.
8. Gurney, K. and Gurney, K.H. (1997) An Introduction to Neural Networks, CRC Press.
9. Holland, J.H. (1975) Adaptation in Natural and Artificial Systems, University of Michigan Press, Michigan.
10. Jagielska, I. (1998) 'Linguistic rule extraction from neural networks for descriptive data mining', Second International Conference on Knowledge Based Intelligent Electronic System, 21-23 April, Adelaide, Australia.
11. Jang, J.S.R., Sun, C-T. and Mizutani, E. (2007) Neuro-Fuzzy and Soft Computing A Computational Approach to Learning and Machine Intelligence, Pearson Education.
12. Janghel, R.R., Shukla, A., Tiwari, R. and Tiwari, P. (2009) 'Clinical decision support system for fetal delivery using artificial neural network', Proceedings of the International Conference on New Trends in Information and Service Science, pp. 1070-1075.
13. Kamruzzaman, S.M., Ryadh, A., Abu, H., Siddiquee, B. and Hoque, Md.E. (2004) 'Medical diagnosis using neural networks', 3rd International Conference on Electrical & Computer Engineering', 28-30 December, Dhaka, Bangladesh, pp. 537-540.
14. Khanna Nehemiah, H., Vijaya, K., Kannan, A. and Sujatha, M. (2007) 'A fuzzy statistical reasoning approach for predicting the risk of cardiovascular diseases', International Journal of Applied Mathematical Analysis and Applications, Vol. 2, Nos. 1-2, pp. 9-21.
15. Kishan, M., Mohan, C.K. and Ranka, S. (1997) Elements of Artificial Neural Networks, Massachusetts Institute of Technology.
16. Hemsathapat, K., Dagli, C.H. and Enke, D. (2001) 'Using a neuro-fuzzy-genetic data mining architecture to determine a marketing strategy in a charitable organization', Donor database, Proceedings of the 1st International Engineering Management Conference, pp. 64-69. (Pubitemid 33075353)
17. Liping, A. and Lingyun, T. (2005) 'A rough neural expert system for medical diagnosis', Proceedings of the International Conference on Servers, Systems and Services Management, Chong Quing, China, Vol. 1, pp. 1130-1135.
18. Lisboa, P.J. and Taktak, A.F.G. (2006) 'The use of artificial neural networks in decision support in cancer: a systematic review', Neural Networks, Vol. 19, No. 4, pp. 408-415. (Pubitemid 44183627)
19. Lisboa, P.J.G. (2002) 'A review of evidence of health benefit from artificial neural networks in medical intervention', Neural Networks, Vol. 15, No. 1, pp. 11-39. (Pubitemid 34234792)
20. Marchant, T. (2007) 'A measurement-theoretic axiomatization of trapezoidal membership functions', IEEE Transactions on Fuzzy Systems, Vol. 15, No. 2, pp. 238-242. (Pubitemid 46563424)
21. Michalewicz, Z. (1992) Genetic Algorithms + Data Structures = Evolution Programs, Springer Verlag, Berlin.
22. Mitchell, M. (1998) An Introduction to Genetic Algorithm, MIT Press.
23. Montana, D.J. and Davis, L. (1992) 'Training feedforward neural networks using genetic algorithms', Proceedings of International Conference on Artificial Intelligence, Detroit, Michigan, Vol. 1, pp. 762-767.
24. Palaniappan, S. and Awang, R. (2008) 'Intelligent heart disease prediction system using data mining techniques' IEEE/ACS International Conference on Computer Systems and Applications, pp. 108-115.
25. Parthiban, L. and Subramanian, R. (2008) 'Intelligent heart disease prediction system using CANFIS and genetic algorithm', International Journal of Biological and Medical Sciences, Vol. 3, No. 3, pp. 157-160.
26. Rajasekaran, S. and Vijayalakshmi Pai, G.A. (2007) Neural Networks, Fuzzy Logic, and Genetic Algorithms Synthesis and Applications, Prentice Hall of India.
27. Rumelhart, D.E. (1980) 'Schemata: the building blocks of cognition', in R.J. Spiro, B. Bruce and W.F. Brewer (Eds.): Theoretical Issues in Reading and Comprehension, Erlbaum, Hillsdale, NJ.
28. Stensmo, M. and Sejnowski, T.J. (1996) 'Automated medical diagnosis based on decision theory and learning from cases', World Congress on Neural Networks International Neural Network Society, pp. 1227-1231.
29. Sumit, B., Praveen, P. and Pillai, G.N. (2008) 'SVM based support system for heart disease classification with integer-coded genetic algorithm to select critical features', Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA.
30. Tsipouras, M.G., Fotiadis, D.I., Exarchos, T.P., Kotsia, A.P., Naka, K.K. and Michalis, L.K. (2008) 'Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling', IEEE Transactions on Information Technology in Biomedicine, Vol. 12, No. 4, pp. 447-458.
31. UCI (2009) Cleveland Heart Disease Dataset, available at http://archive.ics.edu/ml/datasets/Heart+Disease [last accessed 11/02/2010]
32. WHO (2009) World Health Fact Sheet, available at www.who.int/mediacentre/ factsheets/fs317/en [accessed 01/12/2009].
33. Yan, H., Zheng, J., Jiang, Y., Peng, C. and Li, Q. (2003) 'Development of a decision support system for heart disease diagnosis using multilayer perceptron', Proceeding of International Symposium on Circuits and Systems, 27-30 December, Cairo, Egypt.
34. Zadeh, L.A. (1965) 'Fuzzy sets', Information and Control, Vol. 8, No. 3, pp. 338-353.
35. Zhang, J., Jiang, Y. and Yan, H. (2006) 'Committee machines with ensembles of multilayer perceptron for the support of diagnosis of heart diseases', Proceedings of the International Conference on Communications, Circuits and Systems, Vol. 3, pp. 2046-2050. (Pubitemid 351307426)