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A Proposal of a Fuzzy System for Hypertension Diagnosis

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Novel Developments in Uncertainty Representation and Processing

Abstract

One of the most dangerous diseases for humans is the Arterial Hypertension, which this kind of disease that often leads to fatal outcomes, such as heart attack, stroke and renal failure. The hypertension seriously threats the health of the people worldwide. One of the dangerous aspects of the hypertension is that you may not know that you have it. In fact, nearly one-third of people who have high blood pressure don’t know it. The only way to know if the blood pressure is high is through the regular checkups. The evaluation of a patient with Hypertension should (1) confirm the diagnosis of hypertension, (2) detect causes of secondary hypertension y (3) assess cardio vascular risk and organ damage. Therefore, is very important a correct measurement of the blood pressure (BP). Traditionally, office BP measurement has been performed using a sphygmomanometer and stethoscope. Recently, automated office and home BP measurements has been proposed as an alternative to traditional measurement. It has several advantages over manual BP, especially in routine clinical practice. Therefore, we have developed a Fuzzy System for the diagnosis of the Hypertension. Firstly, the input parameters include Systolic Blood Pressure and Diastolic Blood Pressure. Secondly, we have as an output parameter: Blood Pressure Levels (BPL). The input linguistic value includes Low, Low Normal, Normal, High Normal, High, Very High, Too High and Isolated Systolic Hypertension. Finally, we have 14 fuzzy rules to determine the diagnosis output.

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References

  1. Abrishami, Z., Tabatabaee, H.: Design of a fuzzy expert system and a multi-layer neural network system for diagnosis of hypertension. MAGNT Res. Rep. 2(5), 913–926. ISSN: 1444-8939 (2014)

    Google Scholar 

  2. Akinyokun, O.C., Adeniji, O.A.: Experimental study of intelligent computer aided diagnostic and therapy. AMSE J. Model. Simul. Control 27(3), 9–20 (1991)

    Google Scholar 

  3. Azamimi, A.A., Zulkarnay, Z., Nur Farahiyah, M.: Design and development of fuzzy expert system for diagnosis of hypertension. In: IEEE International Conference on Intelligent Systems, Modelling and Simulation (2011)

    Google Scholar 

  4. Das, S., Ghosh, P.K., Kar, S.: Hypertension diagnosis: a comparative study using fuzzy expert system and neuro fuzzy system. In: Fuzzy Systems, IEEE International Conference on. IEEE (2013)

    Google Scholar 

  5. Djam, X.Y., Kimbi, Y.H.: Fuzzy expert system for the management of hypertension. Pac. J. Sci. Technol. 12(1) (2011) (Spring)

    Google Scholar 

  6. Fuller, R., Giove, S.: A Neuro-fuzzy approach to FMOLP problems. In: Proceedings of CIFT’94, pp. 97–101. Trento, Italy (1994)

    Google Scholar 

  7. Kaur, A., Bhardwaj, A.: Genetic neuro fuzzy system for hypertension diagnosis. Int. J. Comput. Sci. Inf. Technol. 5(4), 4986–4989 (2014)

    Google Scholar 

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

    Google Scholar 

  9. Ludmila, I.K., Steimann, F.: Fuzzy Medical Diagnosis. School of Mathematics, University of Wales, Bangor (2008)

    Google Scholar 

  10. Mancia, G., Fagard, R., Narkiewicz, K., Redon, J.: 2013 ESH/ESC guidelines for the management of arterial hypertension. J. Hypertens. 31, 1281–1357 (2013)

    Article  Google Scholar 

  11. Merouani, M., Guignard, B., Vincent, F., Borron, S.W., Karoubi, P., Fosse, J.P., Cohen, Y., Clec’h, C., Vicaut, E., Marbeuf-Gueye, C., Lapostolle, F., Adnet, F.: Can fuzzy logic make things more clear? Crit. Care 13, 116 (2009)

    Article  Google Scholar 

  12. O’Brien, E., Parati, G., Stergiou, G.: European society of hypertension position paper on ambulatory blood pressure monitoring. J. Hypertens. 31, 1731–1768 (2013)

    Google Scholar 

  13. Rahim, F., Deshpande, A., Hosseini, A.: Fuzzy expert system for fluid management in general anesthesia. J. Clin. Diagn. Res. 256–267 (2007)

    Google Scholar 

  14. Srivastava, P:. A note on hypertension classification scheme and soft computing decision making system. ISRN Biomathematics (2013)

    Google Scholar 

  15. Sumathi, B. Santhakumaran, A.: Pre-diagnosis of hypertension using artificial neural network. Glob. J. Comput. Sci. Technol. 11(2) Version 1.0 (2011)

    Google Scholar 

  16. Zadeh, L.A.: Fuzzy sets and systems. In Proceedings Symposium on System Theory. Fox, J. (ed.) Polytechnic Institute of Brooklyn, pp. 29–37. New York, NY. April (1965)

    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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Guzmán, J.C., Melin, P., Prado-Arechiga, G. (2016). A Proposal of a Fuzzy System for Hypertension Diagnosis. In: Atanassov, K., et al. Novel Developments in Uncertainty Representation and Processing. Advances in Intelligent Systems and Computing, vol 401. Springer, Cham. https://doi.org/10.1007/978-3-319-26211-6_29

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  • DOI: https://doi.org/10.1007/978-3-319-26211-6_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26210-9

  • Online ISBN: 978-3-319-26211-6

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