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Fuzzy Classifier Using the Particle Swarm Optimization Algorithm for the Diagnosis of Arterial Hypertension

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Fuzzy Logic and Neural Networks for Hybrid Intelligent System Design

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1061))

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Abstract

The main objective of this article is the creation of a new fuzzy classifier, using the optimization algorithm by means of particles, to optimize the structures of the type-1 and type-2 fuzzy systems, (such as parameters and type of membership functions, type of system, and number of rules).Tests were carried out with 40 patients and the blood pressure readings of the patients were taken at a time interval for 24 h, and these were taken through an ambulatory blood pressure monitor (ABPM). In this work, good results for Classification and Diagnosis of Arterial Hypertension with the proposed model are shown.

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Authors and Affiliations

  1. Tijuana Institute of Technology/TecNM, Tijuana, Mexico

    Martha Pulido & Patricia Melin

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  1. Martha Pulido
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  2. Patricia Melin
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Correspondence to Patricia Melin .

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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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Pulido, M., Melin, P. (2023). Fuzzy Classifier Using the Particle Swarm Optimization Algorithm for the Diagnosis of Arterial Hypertension. In: Castillo, O., Melin, P. (eds) Fuzzy Logic and Neural Networks for Hybrid Intelligent System Design. Studies in Computational Intelligence, vol 1061. Springer, Cham. https://doi.org/10.1007/978-3-031-22042-5_5

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