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170802 ||| eng |
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|a 9783319611495
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|a Melin, Patricia
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| 245 |
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|a New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension
|h Elektronische Ressource
|c by Patricia Melin, German Prado-Arechiga
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| 250 |
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|a 1st ed. 2018
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2018, 2018
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| 300 |
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|a VIII, 88 p. 48 illus., 47 illus. in color
|b online resource
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|a From the Content: Introduction -- Fuzzy Logic for Arterial Hypertension Classification -- Design of a Neuro Design of a Neuro Design of Arterial Hypertension
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| 653 |
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|a Health Informatics
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| 653 |
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|a Biomedical engineering
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Medical informatics
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Biomedical Engineering and Bioengineering
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| 700 |
1 |
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|a Prado-Arechiga, German
|e [author]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a SpringerBriefs in Computational Intelligence
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| 028 |
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|a 10.1007/978-3-319-61149-5
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| 856 |
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|u https://doi.org/10.1007/978-3-319-61149-5?nosfx=y
|x Verlag
|3 Volltext
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|a 006.3
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| 520 |
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|a In this book, a new approach for diagnosis and risk evaluation of ar-terial hypertension is introduced. The new approach was implement-ed as a hybrid intelligent system combining modular neural net-works and fuzzy systems. The different responses of the hybrid system are combined using fuzzy logic. Finally, two genetic algo-rithms 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 pa-tient data show that when the optimization is used, the results can be better than without optimization. This book is intended to be a refer-ence for scientists and physicians interested in applying soft compu-ting techniques, such as neural networks, fuzzy logic and genetic algorithms, in medical diagnosis, but also in general to classification and pattern recognition and similar problems
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