Table of Contents
ISRN Biomathematics
Volume 2013 (2013), Article ID 342970, 11 pages
http://dx.doi.org/10.1155/2013/342970
Research Article

A Note on Hypertension Classification Scheme and Soft Computing Decision Making System

1Department of Mathematics, Motilal Nehru National Institute of Technology, Allahabad 211004, India
2Applied Mechanics Department, Motilal Nehru National Institute of Technology, Allahabad 211004, India
3Department of Mathematics, Dayanand College of Commerce, Latur 413512, India

Received 22 July 2013; Accepted 28 August 2013

Academic Editors: V. P. Emerenciano and J. R. C. Piqueira

Copyright © 2013 Pankaj Srivastava et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. X. Y. Djam and Y. H. Kimbi, “Fuzzy expert system for the management of hypertension,” The Pacific Journal of Science and Technology, vol. 12, no. 1, p. 390, 2011. View at Google Scholar
  2. X. Y. Djam and Y. H. Kimb, “A medical diagnostic support system for the management of Hypertension (MEDDIAG),” Journal of Medical and Applied Biosciences, vol. 3, pp. 41–55, 2011. View at Google Scholar
  3. R. Jain, “Decision making in the presence of fuzzy variables,” IEEE Transactions on Systems, Man and Cybernetics, vol. 6, no. 10, pp. 698–703, 1976. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Poli, S. Cagnoni, R. Livi et al., “A neural network expert systemfor diagnosing and treating hypertension,” Computer, vol. 24, no. 3, pp. 64–71, 1991. View at Publisher · View at Google Scholar
  5. R. Degani, “Computerized electrocardiogram diagnosis: fuzzy approach,” Methods of Information in Medicine, vol. 31, no. 4, pp. 225–233, 1992. View at Google Scholar · View at Scopus
  6. S. Charbonnier, S. Galichet, G. Mauris, and J. P. Siché, “Statistical and fuzzy models of ambulatory systolic blood pressure for hypertension diagnosis,” IEEE Transactions on Instrumentation and Measurement, vol. 49, no. 5, pp. 998–1003, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. R. K. Jena, M. M. Aqel, P. Srivastava, and P. K. Mahanti, “Soft computing methodologies in bioinformatics,” European Journal of Scientific Research, vol. 26, no. 2, pp. 189–203, 2009. View at Google Scholar · View at Scopus
  8. D. Pandey, M. Vaishali, and S. Pankaj, “Rule-based system for cardiac analysis,” National Academy Science Letters, vol. 29, no. 7-8, pp. 299–309, 2006. View at Google Scholar · View at Scopus
  9. N. Allahverdi, S. Torun, and I. Saritas, “Design of a fuzzy expert system for determination of coronary heart disease risk,” in International Conference on Computer Systems and Technologies (CompSysTech '07), June 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. N. Nalayini and R. S. D. Wahidabanu, “Design methodology of a controller to forecast the uncertain cardiac arrest using fuzzy logic approach,” in Proceedings of the International Conference Intelligent Systems and Controls, pp. 46–52, Karpagam College of Engineering, Coimbatore, India, 2008.
  11. P. Srivastava and A. Srivastava, “A soft computing approach for cardiac analysis,” Journal of Basic and Applied Scientific Research, vol. 2, no. 1, pp. 376–385, 2012. View at Google Scholar
  12. P. Srivastava and N. Sharma, “Soft computing criterion for ECG beat classification and cardiac analysis,” communicated, 2012.