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A Hybrid Intelligent System Model for Hypertension Risk Diagnosis

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Fuzzy Logic in Intelligent System Design (NAFIPS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 648))

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Abstract

A hybrid intelligent system is made of a powerful combination of soft computing techniques for reducing the complexity in solving difficult problems. Nowadays hypertension (high blood pressure) has a high prevalence in the world population and is the number one cause of mortality in Mexico. It is sometimes referred to as the silent killer because it often has no symptoms. We design in this paper a hybrid model using modular neural networks, and as a response integrator we use a fuzzy systems to provide an accurate diagnosis of hypertension, so we can prevent a future disease in people based on the systolic pressure, diastolic pressure and pulse of patients with ages between 15 to 95 years.

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References

  1. Fdez Riverola, F., Corchado, J.M.: Forecasting red tides using an hybrid neuro-symbolic system. AI Commun. 16(4), 221–233 (2003)

    MathSciNet  Google Scholar 

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

    Book  MATH  Google Scholar 

  3. Miramontes, I., Martínez, G., Melin, P., Prado-Arechiga, G.: A hybrid intelligent system model for hypertension diagnosis. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Nature-Inspired Design of Hybrid Intelligent Systems, pp. 541–550. Springer International Publishing, Cham (2017)

    Chapter  Google Scholar 

  4. Rosendorff, C.: Essential Cardiology, 3rd edn. Springer, Bronx (2013)

    Book  Google Scholar 

  5. Mancia, G., et al.: 2013 ESH/ESC guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the European Society of hypertension (ESH) and of the European Society of Cardiology (ESC). Eur. Heart J. 34(28), 2159–2219 (2013)

    Article  Google Scholar 

  6. Mancia, G., Grassi, G., Kjeldsen, S.E.: Manual of Hypertension of the European Society of Hypertension. Informa Healthcare, Abingdon (2008)

    Book  Google Scholar 

  7. Krakoff, L.R.: Hypertension: ambulatory blood-pressure monitoring has arrived. Nat. Rev. Cardiol. 8(12), 671–672 (2011)

    Article  Google Scholar 

  8. Wizner, B., Gryglewska, B., Gasowski, J., Kocemba, J., Grodzicki, T.: Normal blood pressure values as perceived by normotensive and hypertensive subjects. J. Hum. Hypertens. 17(2), 87–91 (2003)

    Article  Google Scholar 

  9. Beevers, G., Lip, G.Y.H., O’Brien, E.: ABC of Hypertension, 5th edn. Blackwell Publishing, Malden (2007)

    Google Scholar 

  10. Battegay, E.J., Lip, G.Y.H., Bakris, G.L.: Hypertension: Principles and Practices. Taylor & Francis, Boca Raton (2005)

    Book  Google Scholar 

  11. Pickering, T.G., Shimbo, D., Haas, D.: Ambulatory blood-pressure monitoring. N. Engl. J. Med. 354(22), 2368–2374 (2006)

    Article  Google Scholar 

  12. White, W.B.: Blood Pressure Monitoring in Cardiovascular Medicine and Therapeutics. Humana Press, Totowa (2007)

    Book  Google Scholar 

  13. O’Brien, E., Parati, G., Stergiou, G.: Ambulatory blood pressure measurement. Hypertension 62(6), 988–994 (2013)

    Article  Google Scholar 

  14. Sumathi, B.B.: Pre-diagnosis of hypertension using artificial neural network. 11(2), 390–402 (2011)

    Google Scholar 

  15. Samant, R., Rao, S.: Evaluation of artificial neural networks in prediction of essential hypertention. Int. J. Comput. Appl. 81(12), 34–38 (2013)

    Google Scholar 

  16. Guzmán, J.C., Melin, P., Prado-Arechiga, G.: Design of a fuzzy system for diagnosis of hypertension. In: Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization, pp. 517–526. Springer International Publishing, Cham (2015)

    Google Scholar 

  17. Guzmán, J.C., Melin, P., Prado-Arechiga, G.: Neuro-fuzzy hybrid model for the diagnosis of blood pressure. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Nature-Inspired Design of Hybrid Intelligent Systems, pp. 573–582. Springer International Publishing, Cham (2017)

    Chapter  Google Scholar 

  18. Das, S., Ghosh, P.K.: Hypertension diagnosis: a comparative study using fuzzy expert system and neuro fuzzy system. In: IEEE International Conference on Fuzzy Systems, no. 2005, pp. 1–7 (2013)

    Google Scholar 

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

    Google Scholar 

  20. Kenney, L., Humphrey, R., Mahler, D., Brayant, C.: ACSM’s Guidelines for Exercise Testing and Prescription. Williams & Wilkins, Philadelphia (1995)

    Google Scholar 

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Acknowledgment

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.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Ivette Miramontes, Gabriela Martínez, Patricia Melin & German Prado-Arechiga

Authors
  1. Ivette Miramontes
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  2. Gabriela Martínez
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  3. Patricia Melin
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  4. German Prado-Arechiga
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Corresponding author

Correspondence to Patricia Melin .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Reformat

  5. Laboratory of Computational Intelligence and Automation, University of Waterloo, Waterloo, Ontario, Canada

    William Melek

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Miramontes, I., Martínez, G., Melin, P., Prado-Arechiga, G. (2018). A Hybrid Intelligent System Model for Hypertension Risk Diagnosis. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_22

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  • DOI: https://doi.org/10.1007/978-3-319-67137-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67136-9

  • Online ISBN: 978-3-319-67137-6

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