Advertisement

Design of Interval Type-2 Fuzzy Systems for Classification of Blood Pressure Load

  • Juan Carlos Guzmán
  • Patricia MelinEmail author
  • German Prado-Arechiga
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 827)

Abstract

In this work we will design the interval type-2 fuzzy system (FS), which will be based on the definitions and classifications of the blood pressure loads, which are elaborated by cardiology experts and based on table of ranges, the use of an interval type-2 fuzzy system and the reliability of the FOU (footprint of uncertainty) can allows achieving a 100% effective classification, it should be noted that in previous works experiments with type-1 fuzzy systems have been carried out, the goal is to have a good classification of the blood pressure load which is very important nowadays for a cardiologist, since based on this determine a cardiovascular event, the blood pressure load which indicates that the daytime blood pressure load (% of diurnal readings ≥ 135/85 mmHg) and the nocturnal blood pressure load (% of nocturnal readings ≥ 120/70 mmHg).

Keywords

Fuzzy system Hypertension Diagnosis Load blood pressure 

Notes

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.

References

  1. 1.
    L. Kenney, R. Humphrey, D. Mahler, and C. Brayant, ACSM’s Guidelines for Exercise Testing and Prescription (Williams & Wilkins, 1995)Google Scholar
  2. 2.
    Texas Heart Institute, High Blood Pressure (Hypertension) (2017)Google Scholar
  3. 3.
    G. Mancia, G. Grassi, S.E. Kjeldsen, Manual of Hypertension of the European Society of Hypertension (Informa Healtcare, UK, 2008)CrossRefGoogle Scholar
  4. 4.
    B. Wizner, B. Gryglewska, J. Gasowski, J. Kocemba, T. Grodzicki, Normal blood pressure values as perceived by normotensive and hypertensive subjects. J. Hum. Hypertens. 17(2), 87–91 (2003)CrossRefGoogle Scholar
  5. 5.
    C. Rosendorff, Essential Cardiology, 3rd edn. (Springer, Bronx, NY, USA, 2013)CrossRefGoogle Scholar
  6. 6.
    E.J. Battegay, G.Y.H. Lip, G.L. Bakris, Hypertension: Principles and Practices (Taylor & Francis, Boca Raton, FL, 2005)CrossRefGoogle Scholar
  7. 7.
    O.A. Carretero, S. Oparil, Essential hypertension. Circulation 101(3), 329–335 (2000)CrossRefGoogle Scholar
  8. 8.
    L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefGoogle Scholar
  9. 9.
    P. Melin, O. Castillo, Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing (Springer, Berlin, 2005)CrossRefGoogle Scholar
  10. 10.
    Q. Duodu, J.K. Panford, J. Ben Hafron-Acquah, Designing algorithm for malaria diagnosis using fuzzy logic for treatment (AMDFLT) in Ghana. Int. J. Comput. Appl. 91(17), 22–27 (2014)Google Scholar
  11. 11.
    I. Morsi, Y.Z. Abd El Gawad, Fuzzy logic in heart rate and blood pressure measuring system, in IEEE Sensors Applications Symposium Proceedings (2013), pp. 113–117Google Scholar
  12. 12.
    R. Nohria, P.S. Mann, Diagnosis of hypertension using adaptive neuro-fuzzy inference system. Int. J. Comput. Sci. Technol. 8491, 36–40 (2015)Google Scholar
  13. 13.
    S. Sikchi, S. Sikchi, M. Ali, Design of fuzzy expert system for diagnosis of cardiac diseases. Int. J. Med. Sci. Public Health 2(1), 56 (2013)CrossRefGoogle Scholar
  14. 14.
    O. Oparaku, E. Udo, Fuzzy logic system for fetal heart rate determination. Int. J. Eng. Res. 5013(4), 60–63 (2015)Google Scholar
  15. 15.
    A.A. Sadat Asl, M.H.F. Zarandi, A Type-2 fuzzy expert system for diagnosis of leukemia, in Fuzzy Logic in Intelligent System Design (2018), pp. 52–60Google Scholar
  16. 16.
    S. Sotudian, M.H.F. Zarandi, I.B. Turksen, From Type-I to Type-II fuzzy system modeling for diagnosis of hepatitis. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 10(7), 1280–1288 (2016)Google Scholar
  17. 17.
    V. Pabbi, Fuzzy expert system for medical diagnosis. Int. J. Sci. Res. Publ. 5(1), 1–7 (2015)Google Scholar
  18. 18.
    K.A. Mohamed, E.M. Hussein, Malaria parasite diagnosis using fuzzy logic. Int. J. Sci. Res. 5(6), 2015–2017 (2016)Google Scholar
  19. 19.
    I. Miramontes, G. Martínez, P. Melin, G. Prado-Arechiga, A hybrid intelligent system model for hypertension risk diagnosis, in Fuzzy Logic in Intelligent System Design (2018), pp. 202–213Google Scholar
  20. 20.
    P. Melin, I. Miramontes, G. Prado-Arechiga, A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis. Expert Syst. Appl. 107, 146–164 (2018)CrossRefGoogle Scholar
  21. 21.
    I. Miramontes, G. Martínez, P. Melin, G. Prado-Arechiga, A hybrid intelligent system model for hypertension diagnosis, in Nature-Inspired Design of Hybrid Intelligent Systems, ed. by P. Melin, O. Castillo, J. Kacprzyk (Springer International Publishing, Cham, 2017), pp. 541–550Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Juan Carlos Guzmán
    • 1
  • Patricia Melin
    • 1
    Email author
  • German Prado-Arechiga
    • 2
  1. 1.Tijuana Institute of TechnologyTijuanaMexico
  2. 2.Excel Medical CenterTijuanaMexico

Personalised recommendations