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Algorithms 2017, 10(3), 79; https://doi.org/10.3390/a10030079

Design of an Optimized Fuzzy Classifier for the Diagnosis of Blood Pressure with a New Computational Method for Expert Rule Optimization

1
Tijuana Institute of Technology, Calzada Tecnologico s/n, Fracc. Tomas Aquino, Baja California, Tijuana 22379, Mexico
2
Cardiodiagnostico, Excel Medical Center, Tijuana 22379, Mexico
*
Author to whom correspondence should be addressed.
Received: 25 May 2017 / Revised: 4 July 2017 / Accepted: 7 July 2017 / Published: 14 July 2017
(This article belongs to the Special Issue Extensions to Type-1 Fuzzy Logic: Theory, Algorithms and Applications)
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Abstract

A neuro fuzzy hybrid model (NFHM) is proposed as a new artificial intelligence method to classify blood pressure (BP). The NFHM uses techniques such as neural networks, fuzzy logic and evolutionary computation, and in the last case genetic algorithms (GAs) are used. The main goal is to model the behavior of blood pressure based on monitoring data of 24 h per patient and based on this to obtain the trend, which is classified using a fuzzy system based on rules provided by an expert, and these rules are optimized by a genetic algorithm to obtain the best possible number of rules for the classifier with the lowest classification error. Simulation results are presented to show the advantage of the proposed model. View Full-Text
Keywords: neural networks; genetic algorithms; fuzzy logic; blood pressure neural networks; genetic algorithms; fuzzy logic; blood pressure
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Guzman, J.C.; Melin, P.; Prado-Arechiga, G. Design of an Optimized Fuzzy Classifier for the Diagnosis of Blood Pressure with a New Computational Method for Expert Rule Optimization. Algorithms 2017, 10, 79.

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