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, and this is why it is called a silent killer because it often has no symptoms. We design in this paper a hybrid model using modular neural networks, and as response integrator we use fuzzy systems to provide an accurate diagnosis of hypertension, so we can prevent future diseases in people based on the systolic pressure, diastolic pressure, and pulse of patients with ages between 15 and 95 years.
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F. Fdez Riverola and J. M. Corchado, “Sistemas híbridos neuro-simbólicos: Una revisión,” Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, vol. 4, pp. 12 - 26, 2000.
P. Melin and O. Castillo, Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing, Springer, Ed., Germany: Springer, 2005.
J. F. Guadalajara Boo, Cardiología, Mexico, D.F: Méndez Editores, 2006.
G. Mancia, “2013 ESH/ESC Guidelines for the management of arterial hypertension,” Journal of Hypertension, vol. 31, no. 7, pp. 1281-1357, 2013.
J. Narro Robles, O. Rivero Serrano and J. López Bárcena, Diagnóstico y tratamiento en la práctica médica, México: Manual moderno, 2010.
T. Pickering, P. Daichi Shimbo and D. Haas, “Ambulatory Blood-Pressure Monitoring,” New England Journal of Medicine, vol. 354, no. 22, pp. 2368-2374, 2006.
W. White, Blood pressure monitoring in cardiovascular medicine and therapeuctis, Totowa, New Jersey: Humana Press, 2007.
B. Sumathi and A. Santhakumaran, “Pre-Diagnosis of Hypertension Using Artifical Neural Network,” Global Journal of Computer Scinece and Technology, vol. 11, pp. 43-47, 2011.
H. Shuqiong, X. Yihua, Y. Li and W. Sheng, “Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area,” Hypertension Research, vol. 33, pp. 722-726, 2010.
S. Hosseini, C. Jutten and S. Charbonnier, “Neural network modeling of ambulatory systolic blood pressure for hypertension diagnosis,” in Artificial Neural Nets Problem Solving Methods, Maó, Menorca, Spain, Springer, 2003, pp. 599-606.
J. C. Guzman, P. Melin and G. Prado-Arechiga, “Design of a Fuzzy System for Diagnosis of Hypertension,” Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization, vol. 601, pp. 517- 526, 2015.
M. Barman and J. Choudhury, “A Fuzzy Rule Base System for the Diagnosis of Heart Disease,” International Journal of Computer Applications, pp. 46-53, 2012.
P. Patil and S. Mohsin, “Fuzzy Logic based Health Care System using Wireless Body Area Network,” International Journal of Computer Applications, vol. 80, no. 12, pp. 46-51, 2013.
I. Morsi and Z. Abd El Gawad, “Fuzzy logic in heart rate and blood pressure measuring system,” in Sensors Applications Symposium (SAS), 2013 IEEE, Galveston, TX, IEEE, 2013, pp. 113-117.
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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Miramontes, I., Martínez, G., Melin, P., Prado-Arechiga, G. (2017). A Hybrid Intelligent System Model for Hypertension Diagnosis. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_35
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DOI: https://doi.org/10.1007/978-3-319-47054-2_35
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