New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Science & Nature, Technology, Engineering, General Computing
Cover of the book New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension by Patricia Melin, German Prado-Arechiga, Springer International Publishing
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Author: Patricia Melin, German Prado-Arechiga ISBN: 9783319611495
Publisher: Springer International Publishing Publication: July 4, 2017
Imprint: Springer Language: English
Author: Patricia Melin, German Prado-Arechiga
ISBN: 9783319611495
Publisher: Springer International Publishing
Publication: July 4, 2017
Imprint: Springer
Language: English

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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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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