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Hybrid Neural-Fuzzy Modeling and Classification System for Blood Pressure Level Affectation

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

Part of the book series: Studies in Computational Intelligence ((SCI,volume 827))

Abstract

In the recent years, the technological advancements tend to leave people with the necessity to stay in a sedentary position for several hours in order to achieve their work objectives. One of the parameters involving health issues tends to do with blood pressure. These are measurements that involve the circulation of the blood throughout the human body. We are developing a modeling and classification of hybrid system to evaluate and see the tendency of how much does a sedentary state affect while working in front of a computer for more than 8 h. The main idea is that the system learns the behavior or the trend of the different blood pressure that are extracted from individuals that fit this experiment. A modular network will be used for the part of modeling, while using fuzzy module to help us determine the best practical classification process in order to obtain a more precise affection of blood pressure.

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, B.C., Mexico

    Martin Vázquez & Patricia Melin

  2. Excel Medical Center, Tijuana, Mexico

    German Prado-Arechiga

Authors
  1. Martin Vázquez
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  2. Patricia Melin
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  3. German Prado-Arechiga
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Corresponding author

Correspondence to Patricia Melin .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

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Vázquez, M., Melin, P., Prado-Arechiga, G. (2020). Hybrid Neural-Fuzzy Modeling and Classification System for Blood Pressure Level Affectation. In: Castillo, O., Melin, P. (eds) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Studies in Computational Intelligence, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-030-34135-0_18

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