Skip to main content
SpringerLink
Log in
Book cover

New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension pp 49–62Cite as

Design of Modular Neural Network for Arterial Hypertension Diagnosis

  • Chapter
  • First Online:

  • 300 Accesses

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSINTELL))

Abstract

In this chapter, a method is proposed to diagnose the blood pressure of a patient (Systolic pressure, diastolic pressure and pulse). This method consists of a modular neural network and its response with average integration. The proposed approach consists on applying these methods to find the best architecture of the modular neural network and the lowest prediction error. Simulations results show that the modular network produces a good diagnostic of the blood pressure of a patient.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. F. Fernandez Riverola, J.M. Corchado, Sistemas híbridos neuro-simbólicos: Una revisión. Inteligencia Artificial Revista Iberoamericana de, Inteligencia Artificial 4, 12–26 (2000)

    Google Scholar 

  2. P. Melin, O. Castillo, Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing (Springer, Germany, 2005)

    Google Scholar 

  3. J.C. Guzman, P. Melin, G. Prado Arechiga, Design of a fuzzy system for diagnosis of hypertension, in Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization (2015), pp. 517–526

    Google Scholar 

  4. B. Sumathi, A. Santhakumaran, Pre-diagnosis of hypertension using artificial neural network. Glob. J. Comput. Sci. Technol. 11, 43–48 (2011)

    Google Scholar 

  5. S. Huang, Y. Xu, L. Yue, S. Wei, L. Liu, X. Gan, S. Zhou, S. Nie, Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area. Hypertens. Res. 33, 722–726 (2010)

    Article  Google Scholar 

  6. V.G. Vilkov, R.G. Oganov, S.A. Shal’nova, Comparative accuracy of neural network models for diagnosing latent arterial hypertension on the basis of data on daily blood. Hum. Phys. 32, 33–51 (2006)

    Google Scholar 

  7. R. Kaur, A. Kaur, Hypertension diagnosis using fuzzy expert system, in International Journal of Engineering Research and Applications (IJERA), National Conference on Advances in Engineering and Technology, AET, 29 March 2014. ISSN: 2248–9622 (2014)

    Google Scholar 

  8. A Azian, Abdullah Z, Zakaria M, Farahiyah M, Design and development of Fuzzy Expert System for diagnosis of hypertension. in International Conference on Intelligent Systems, Modelling and Simulation, University of Malaysia Perils, Jejawi, Malaysia, IEEE, vol 56 (2011), pp. 5–6

    Google Scholar 

  9. X.Y. Djam, Y.H. Kimbi, Fuzzy expert system for the management of hypertension. The Pac J Sci Technol 11, 1 (2011)

    Google Scholar 

  10. M. Barman, J. Choudhury, A Fuzzy rule base system for the diagnosis of heart disease. Int. J. Comput. Appl., 46–53 (2012)

    Google Scholar 

  11. P. Patil, S. Mohsin, Fuzzy logic based health care system using wireless body area network. Int. J. Comput. Appl. 80(12), 46–51 (2013)

    Google Scholar 

  12. I. Morsi, Z.A. El Gawad, Fuzzy logic in heart rate and blood pressure measuring system, in Sensors Applications Symposium (SAS), 2013 IEEE, Galveston, TX, IEEE, 2013, pp. 113–117

    Google Scholar 

  13. S. Hosseini, C. Jutten, S. Charbonnier, Neural network modeling of ambulatory systolic blood pressure for hypertension diagnosis, Artificial Neural Nets Problem Solving Methods (Springer, Maó, Menorca, Spain, 2003), pp. 599–606

    Chapter  Google Scholar 

  14. R. Nohria, P.S. Mann, Diagnosis of hypertension using adaptive neuro-fuzzy inference system. Int. J. Comput. Sci. Technol. 6, 2229–4333 (2015)

    Google Scholar 

  15. A. Kaur, A. Bhardwaj, Genetic neuro fuzzy system for hypertension diagnosis. Int. J. Comput. Sci. Inf. Technol. 5, 4986–4989 (2014)

    Google Scholar 

  16. A. Zeinab, T. Hamid, Design of a fuzzy expert system and a multi-layer neural network 292 system for diagnosis of hypertension (Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran), 2(5), 913–926 (2015)

    Google Scholar 

  17. S. Das, P.K. Ghosh, S. Kar, Hypertension diagnosis: A comparative study using fuzzy expert system and Neuro Fuzzy System, in IEEE Conference Publications (2013), pp. 1–7

    Google Scholar 

  18. I.M. Multaba, M.A. Hussain, Application of Neural Networks and Other Learning Technologies in Process Engineering (Imperial Collage Press, 2001)

    Google Scholar 

  19. M. Meltser, M. Shoham, L. Manevitz, Approximating functions by neural networks: A constructive solution in the uniform norm. Neural Netw. 9(6), 965–978 (1996)

    Article  Google Scholar 

Download references

Acknowledgements

We would like to express our gratitude to the CONACYT and Tijuana Institute of Technology for the facilities and resources granted for the development of this research. We also would like to thank the Cardiodiagnostico of the Excel Medical Center in Tijuana, Mexico for the guidelines and resources given for this research project.

Author information

Authors and Affiliations

  1. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  2. Cardiodiagnostico, Excel Medical Center, Tijuana, Mexico

    German Prado-Arechiga

Authors
  1. Patricia Melin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. German Prado-Arechiga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patricia Melin .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 The Author(s)

About this chapter

Cite this chapter

Melin, P., Prado-Arechiga, G. (2018). Design of Modular Neural Network for Arterial Hypertension Diagnosis. In: New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-61149-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61149-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61148-8

  • Online ISBN: 978-3-319-61149-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation