New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension
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....
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| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | English |
| Vydavateľské údaje: |
Cham :
Springer International Publishing,
2018.
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| Vydanie: | 1st ed. 2018. |
| Edícia: | SpringerBriefs in Computational Intelligence,
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| Predmet: | |
| ISBN: | 9783319611495 |
| ISSN: | 2625-3704 |
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| 008 | 170704s2018 gw | s |||| 0|eng d | ||
| 020 | |a 9783319611495 | ||
| 024 | 7 | |a 10.1007/978-3-319-61149-5 |2 doi | |
| 035 | |a CVTIDW12106 | ||
| 040 | |a Springer-Nature |b eng |c CVTISR |e AACR2 | ||
| 041 | |a eng | ||
| 100 | 1 | |a Melin, Patricia. |4 aut | |
| 245 | 1 | 0 | |a New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension |h [electronic resource] / |c by Patricia Melin, German Prado-Arechiga. |
| 250 | |a 1st ed. 2018. | ||
| 260 | 1 | |a Cham : |b Springer International Publishing, |c 2018. | |
| 300 | |a VIII, 88 p. 48 illus., 47 illus. in color. |b online resource. | ||
| 490 | 1 | |a SpringerBriefs in Computational Intelligence, |x 2625-3704 | |
| 500 | |a Engineering | ||
| 505 | 0 | |a From the Content: Introduction -- Fuzzy Logic for Arterial Hypertension Classification -- Design of a Neuro Design of a Neuro Design of Arterial Hypertension. | |
| 516 | |a text file PDF | ||
| 520 | |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. | ||
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Biomedical engineering. | |
| 650 | 0 | |a Health informatics. | |
| 856 | 4 | 0 | |u http://hanproxy.cvtisr.sk/han/cvti-ebook-springer-eisbn-978-3-319-61149-5 |y Vzdialený prístup pre registrovaných používateľov |
| 910 | |b ZE09386 | ||
| 919 | |a 978-3-319-61149-5 | ||
| 974 | |a andrea.lebedova |f Elektronické zdroje | ||
| 992 | |a SUD | ||
| 999 | |c 238549 |d 238549 | ||