Authors:
- Presents a new approach for diagnosis and risk evaluation of arterial hypertension
- Demonstrates the implementation of the approach as a hybrid intelligent system combining modular neural networks and fuzzy systems
- Two genetic algorithms are used to perform the optimization of the modular neural networks parameters and fuzzy inference system parameters
- The experimental results obtained using the proposed method on real patient data show that when the optimization is used, the results can be better than without optimization
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
Authors and Affiliations
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Division of Graduate Studies, Tijuana Institute of Technology Division of Graduate Studies, Tijuana, Mexico
Patricia Melin
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Cardiodiagnostico, Excel Medical Center Cardiodiagnostico, Tijuana, Mexico
German Prado-Arechiga
Bibliographic Information
Book Title: New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension
Authors: Patricia Melin, German Prado-Arechiga
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-319-61149-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s) 2018
Softcover ISBN: 978-3-319-61148-8Published: 12 July 2017
eBook ISBN: 978-3-319-61149-5Published: 04 July 2017
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: VIII, 88
Number of Illustrations: 1 b/w illustrations, 47 illustrations in colour
Topics: Computational Intelligence, Biomedical Engineering and Bioengineering, Health Informatics