Titlebook: New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension; Patricia Melin,German Prado-Arechiga Book 2018

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書目名稱New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension讀者反饋




書目名稱New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension讀者反饋學(xué)科排名

CHAFE

得罪

Fuzzy Logic for Arterial Hypertension Classification,ameters include Systolic Blood Pressure and Diastolic Blood Pressure. Secondly, we have as an output parameter: Blood Pressure Levels (BPL). The input linguistic values include Low, Normal Low, Normal, Normal High, High, Very High, Too High and Isolated Systolic Hypertension. Finally, we have 14 fuzzy rules to determine out diagnosis.

Flagging

Design of a Neuro-Fuzzy System for Diagnosis of Arterial Hypertension,nt patients. The fuzzy expert system is based on a set of inputs and rules. The input variables for this system are the systolic and diastolic pressures and the output variable is the blood pressures level. It is expected that this proposed neuro-fuzzy hybrid model can provide a faster, cheaper and more accurate result.

占卜者

Book 2018than 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.

Peristalsis

背景

Conclusions,portance of developing new methods using Computational Intelligence for application in medicine, particularly in the area of cardiology to diagnose cardiovascular diseases. In this particular case to help medical doctors diagnose, classify and determine the possible risk of developing high blood pressure.

Abduct

wreathe

Book 2018intelligent 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.

STALL

2191-530X sicians 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.978-3-319-61148-8978-3-319-61149-5Series ISSN 2191-530X Series E-ISSN 2191-5318