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2018 | Book

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

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About this book

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.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
In the book we present a novel model for classification, diagnosis and risk evaluation of high blood pressure using new hybrid intelligent systems, combining Modular Neural Networks, Fuzzy Logic and Genetic Algorithms. We focused on the development of hybrid intelligent systems; for classification of blood pressure levels using the experience of cardiologists and the guidelines of European Society of Cardiology, and for constructing a fuzzy logic classification method based on patient’s Blood pressure.
Patricia Melin, German Prado-Arechiga
Chapter 2. Fuzzy Logic for Arterial Hypertension Classification
Abstract
One of the most dangerous diseases for humans is the high blood pressure (HBP) or hypertension, which is a kind of disease that often leads to fatal outcomes, such as heart attack, stroke and renal failure. The HBP seriously threats the health of people worldwide. One of the dangerous aspects is that people may not know that they have it. In fact, nearly one-third of people who have high blood pressure don’t know it. The only way to know if the blood pressure is high is through the regular checkups. Therefore, we have developed a Fuzzy System for the diagnosis of the HBP. Firstly, the input parameters 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.
Patricia Melin, German Prado-Arechiga
Chapter 3. Design of a Neuro-Fuzzy System for Diagnosis of Arterial Hypertension
Abstract
We propose a neuro-fuzzy hybrid model for the diagnosis of high blood pressure or hypertension to provide a diagnosis as accurate as possible based on intelligent computing techniques, such as neural networks and fuzzy logic. The neuro-fuzzy model uses a modular architecture, which works with different number of layers and different learning parameters so we can have a more accurate modeling. So for the better diagnosis and treatment of hypertension patients, an intelligent and accurate system is needed. In this study, we also design a fuzzy expert system to diagnose blood pressure for different 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.
Patricia Melin, German Prado-Arechiga
Chapter 4. Neuro-Fuzzy Modular Approaches for Classification of Arterial Hypertension with a Method for the Expert Rules Optimization
Abstract
A Neuro fuzzy hybrid model (NFHM) is used as a new Artificial Intelligence method to classify high blood pressure (HBP). The NFHM uses techniques such as: neural networks, fuzzy logic and evolutionary computation, in the last technique genetic algorithms (GAs) are used. The objective is to model the behavior of blood pressure based on monitoring data of 24 h per patient and to obtain the trend, which is classified using a fuzzy system based on rules given by an expert, these rules were optimized by a genetic algorithm to obtain the best possible number of rules for the classifier with the lowest classification error. Results are presented to show the advantage of the proposal model.
Patricia Melin, German Prado-Arechiga
Chapter 5. Design of Modular Neural Network for Arterial Hypertension Diagnosis
Abstract
In this chapter, a method is proposed to diagnose the blood pressure of a patient (Systolic pressure, diastolic pressure and pulse). This method consists of a modular neural network and its response with average integration. The proposed approach consists on applying these methods to find the best architecture of the modular neural network and the lowest prediction error. Simulations results show that the modular network produces a good diagnostic of the blood pressure of a patient.
Patricia Melin, German Prado-Arechiga
Chapter 6. Intelligent System for Risk Estimation of Arterial Hypertension
Abstract
A hybrid intelligent system is constructed of a powerful combination of soft computing techniques for reducing the complexity in solving difficult problems. Nowadays Cardiovascular Diseases, like arterial hypertension (high blood pressure) has a high prevalence in the world population. They are the number one cause of mortality in Mexico, and that is why the HBP is called a silent killer because it often has no symptoms. We design in this research work a hybrid model using modular neural networks, and as response integrator we use fuzzy systems to provide an accurate risk diagnosis of hypertension, so we can prevent a futures disease in people based on the systolic pressure, diastolic pressure and pulse of patients with ages between 15 and 95 years.
Patricia Melin, German Prado-Arechiga
Chapter 7. Conclusions
Abstract
We present in this book a novel model for classification, diagnosis and risk evaluation of high blood pressure or arterial hypertension using new hybrid intelligent systems, combining Modular Neural Networks, Fuzzy Logic and Genetic Algorithms. The motivation of this research work is based on the importance 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.
Patricia Melin, German Prado-Arechiga
Backmatter
Metadata
Title
New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension
Authors
Patricia Melin
German Prado-Arechiga
Copyright Year
2018
Electronic ISBN
978-3-319-61149-5
Print ISBN
978-3-319-61148-8
DOI
https://doi.org/10.1007/978-3-319-61149-5

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