Abstract
Nowadays, blood pressure is the most common way to diagnose hypertension, however it is important to observe all the data provided by a 24-h device, which is why it is important to analyze the blood pressure load, which indicates the daytime blood pressure load (% of diurnal readings ≥135/85 mmHg) and the nocturnal blood pressure load (% of nocturnal readings ≥120/70 mmHg). Different studies have shown the correlation between the blood pressure load and some cardiovascular problems. In this work we analyze the day and night load of 30 patients, which were classified with 100% accuracy by the fuzzy classifier and indicated a high index of people with a pressure load and this indicates that a cardiovascular event could occur at any time for these patients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zadeh, L.A.: Fuzzy sets and systems. In: Fox, J. (ed.) Proceedings Symposium on System Theory. Polytechnic Institute of Brooklyn, New York, NY, pp. 29–37 (April 1965)
Zachariah, P.K., Sheps, S.G., Ilstrup, D.M., Long, C.R., Bailey, K.R., Wiltgen, C.M., Carlson, C.A.: Blood pressure load—a better determinant of hypertension. In Mayo Clinic Proceedings, vol. 63, no. 11, pp. 1085–1091. Elsevier (Nov 1988)
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)
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)
Abdullah, A.A., Zulkarnay, Z., Mohammad, N.F.: Design and development of fuzzy expert system for diagnosis of hypertension. In: International Conference on Intelligent Systems, Modelling and Simulation, IEEE (2011)
Das, S., Ghosh, P.K., Kar, S.: Hypertension diagnosis: a comparative study using fuzzy expert system and neuro fuzzy sytem. In: IEEE International Conference on Fuzzy Systems. IEEE (2013)
Djam, X.Y., Kimbi, Y.H.: Fuzzy expert system for the management of hypertension. Pac. J. Sci. Technol. 12(1) (May 2011) (Spring)
Guzmán, J.C., Melin, P., Prado-Arechiga, G.: Design of a fuzzy system for diagnosis of hypertension. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol. 601. Springer, Cham (2015)
Kaur, R., Kaur, A.: Hypertension diagnosis using fuzzy expert system. Int/ J. Eng. Res. Appl. (IJERA) 4(3), 14–18 (2014). ISSN: 2248-9622
Ludmila, I.K., Steimann, F.: Fuzzy Medical Diagnosis. School of Mathematics, University of Wales, Banggor, UK (2008)
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)
White, W.B., Dey, H.M., Schulman, P.: Assessment of the daily blood pressure load as a determinant of cardiac function in patients with mild-to-moderate hypertension. Am. Heart J. 118(4), 782–795 (1989)
Rahim, F., Deshpande, A., Hosseini, A.: Fuzzy expert system for fluid management in general anesthesia. J. Clin. Diagn. Res., 256–267 (2007)
Srivastava, P.: A note on hypertension classification scheme and soft computing decision making system. ISRN Biomath. (2013)
Sumathi, B., Santhakumaran, A.: Pre-diagnosis of hypertension using artificial neural network. Glob. J. Comput. Sci. Technol. 11(2), Version 1.0 (Feb 2011)
Acknowledgements
We would like to express our gratitude to the CONACYT and Tecnologico Nacional de Mexico/Tijuana Institute of Technology for the facilities and resources granted for the development of this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Guzmán, J.C., Melin, P., Prado-Arechiga, G. (2020). Design of a Fuzzy System for Classification of Blood Pressure Load. In: Kóczy, L., Medina-Moreno, J., Ramírez-Poussa, E., Šostak, A. (eds) Computational Intelligence and Mathematics for Tackling Complex Problems. Studies in Computational Intelligence, vol 819. Springer, Cham. https://doi.org/10.1007/978-3-030-16024-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-16024-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16023-4
Online ISBN: 978-3-030-16024-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)