Fuzzy Optimized Classifier for the Diagnosis of Blood Pressure Using Genetic Algorithm

  • Juan Carlos Guzmán
  • Patricia MelinEmail author
  • German Prado-Arechiga
Part of the Studies in Computational Intelligence book series (SCI, volume 749)


We propose to optimize the fuzzy rules, which are based on an expert, the objective is to classify the blood pressure level in a correct way with the necessary number of rules and not to have some type of mistake at the moment of giving the diagnosis, since the use of unnecessary rules could cause a confusion in the fuzzy classifier. The fuzzy classifier is only part of the neuro fuzzy hybrid model, which uses techniques such as: neural networks, fuzzy logic and evolutionary computation, in this latter technique, genetic algorithms are used, which use individuals as possible solutions and thus obtain the best solution, in this case find the appropriate number of fuzzy rules for fuzzy system. This study aims to model blood pressure for 24 h and obtain the trend per patient, once this trend is obtained, this information enters a fuzzy system based on rules given by an expert, who will be classified into some of the blood pressure levels based on level European guide and finally give us a diagnosis per patient.


Fuzzy system Blood pressure Diagnosis 



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.


  1. 1.
    A.M. Abdelbar, S. Abdelshahid, D.C. Wunsch, Fuzzy PSO: A generalization of particle swarm optimization, in Proceedings of International Joint Conference on Neural Networks, vol. 2, pp. 1086–1091 (2005)Google Scholar
  2. 2.
    A.A. Abdullah, Z. Zakaria, N.F. Mohammad, Design and development of fuzzy expert system for diagnosis of hypertension, in Proceedings of 2011 International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2011, pp. 113–117 (2011)Google Scholar
  3. 3.
    Z. Abrishami, I. Azad, Design of a fuzzy expert system and a multi-layer neural network system for diagnosis of hypertension, vol. 4, pp. 138–145, October 2015Google Scholar
  4. 4.
    F. Başçiftçi, A. Eldem, Using reduced rule base with expert system for the diagnosis of disease in hypertension. Med. Biol. Eng. Comput. 51(12), 1287–1293 (2013)CrossRefGoogle Scholar
  5. 5.
    S. Das, P.K. Ghosh, Hypertension diagnosis : a comparative study using fuzzy expert system and neuro fuzzy system, in Proceeding of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Hyderabad, July 7–10, 2013, pp. 1–7Google Scholar
  6. 6.
    X.Y. Djam, Y.H. Kimbi, Fuzzy expert system for the management of hypertension. Pac. J. Sci. Technol. 12(1), pp. 390–402 (2011)Google Scholar
  7. 7.
    A. Kaur, A. Bhardwaj, Genetic neuro fuzzy system for hypertension. Int. J. Comput. Sci. Inf. Technol. 5(4), 4986–4989 (2014)Google Scholar
  8. 8.
    R. Kaur, A. Kaur, Hypertension diagnosis using fuzzy expert system, in International Journal Engineering Research and Applications, pp. 14–18 (2014)Google Scholar
  9. 9.
    G. Mancia et al., 2013 ESH/ESC guidelines for the management of arterial hypertension. Blood Press. 22(4), 193–278 (2013)CrossRefGoogle Scholar
  10. 10.
    P. Melin, J.C. Guzman, G. Prado-Arechiga, [PP.08.10] Artificial intelligence utilizing neuro-fuzzy hybrid model for the classification of blood pressure. J. Hypertens. 34 (2016)Google Scholar
  11. 11.
    P. Melin, G. Prado-Arechiga, J.C. Guzman, PS 05-07 Classification of blood pressure based on a neuro-fuzzy hybrid computational model. J. Hypertens. 34 (2016)Google Scholar
  12. 12.
    P. Melin, G. Prado-Arechiga, M. Pulido, I. Miramontes, OS 26-01 Classification of arterial hypertension using a computational model based on artificial modular neural networks. J. Hypertens. 34 (2016)Google Scholar
  13. 13.
    P. Srivastava, A. Srivastava, A. Burande, A. Khandelwal, A note on hypertension classification scheme and soft computing decision making system. ISRN Biomath. 2013, 11 (2013)zbMATHGoogle Scholar
  14. 14.
    B.B. Sumathi, Pre-diagnosis of hypertension using artificial neural network. 11(2) (2011)Google Scholar
  15. 15.
    F. Valdez, P. Melin, O. Castillo, Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making, in IEEE International Conference on Fuzzy Systems, pp 2114–2119 (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2018

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 CenterPhiladelphiaUSA

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