Abstract
We propose a neuro-fuzzy hybrid model for the diagnosis of blood pressure to provide a diagnosis as accurate as possible based on intelligent computing techniques, such as neural networks and fuzzy logic. The neuro-fuzzy model uses a modular architecture which works with different number of layers and different learning parameters so that we can have a more accurate modeling. So for the better diagnosis and treatment of hypertension patients, an intelligent and accurate system is needed. In this study, we also design a fuzzy expert system to diagnose blood pressure for different patients. The fuzzy expert system is based on a set of inputs and rules. The input variables for this system are the systolic and diastolic pressures and the output variable is the blood pressures level. It is expected that this proposed neuro-fuzzy hybrid model can provide a faster, cheaper, and more accurate result.
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Acknowledgments
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. (2017). Neuro-Fuzzy Hybrid Model for the Diagnosis of Blood Pressure. 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_37
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DOI: https://doi.org/10.1007/978-3-319-47054-2_37
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