This book is focused on the use of intelligent techniques, such as fuzzy logic, neural networks and bio-inspired algorithms, and their application in medical diagnosis. The main idea is that the proposed method may be able to adapt to medical diagnosis problems in different possible areas of the medicine and help to have an improvement in diagnosis accuracy considering a clinical monitoring of 24 hours or more of the patient. In this book, tests were made with different architectures proposed in the different modules of the proposed model. First, it was possible to obtain the architecture of the fuzzy classifiers for the level of blood pressure and for the pressure load, and these were optimized with the different bio-inspired algorithms (Genetic Algorithm and Chicken Swarm Optimization). Secondly, we tested with a local database of 300 patients and good results were obtained. It is worth mentioning that this book is an important part of the proposed general model; for this reason, we consider that these modules have a good performance in a particular way, but it is advisable to perform more tests once the general model is completed.
Author(s): Patricia Melin; Juan Carlos Guzmán; German Prado-Arechiga
Series: SpringerBriefs in Applied Sciences and Technology
Publisher: Springer
Year: 2020
Language: English
Pages: 103
City: Cham
Preface
Contents
1 Introduction to Neuro Fuzzy Hybrid Model
References
2 Theory and Background of Medical Diagnosis
2.1 Blood Pressure
2.1.1 Type of Blood Pressure Diseases
2.1.2 Hypotension
2.1.3 Hypertension
2.1.4 Risk Factors
2.1.5 Home Blood Pressure Monitoring
2.1.6 Ambulatory Blood Pressure Monitoring (ABPM)
2.2 Computational Intelligence Techniques
2.2.1 Genetic Algorithms
2.2.2 Chicken Swarm Optimization
2.2.3 Neural Networks
2.2.4 Fuzzy Logic
References
3 Proposed Neuro Fuzzy Hybrid Model
3.1 General and Specific Neuro Fuzzy Hybrid Models
3.2 Creation of the Modular Neural Network
4 Study Cases to Test the Neuro Fuzzy Hybrid Model
4.1 Design of the Fuzzy Systems for Classification
4.1.1 Design of the First Fuzzy Classifier for the Classification of Blood Pressure Levels
4.1.2 Design of the Second Fuzzy Classifier for the Classification of Blood Pressure Levels
4.1.3 Design of the Third Fuzzy Classifier for the Classification of Blood Pressure Levels
4.1.4 The Optimization of the Fuzzy System Using a Genetic Algorithm (GA)
4.1.5 Design of the Fuzzy Classifier Fourth Optimized with a GA
4.1.6 Knowledge Representation of the Fuzzy Systems
4.1.7 Results of the Proposed Method
4.1.8 Comparison of Results
4.2 A Comparative Study Between European Guidelines and American Guidelines Using Fuzzy Systems for the Classification of Blood Pressure
4.2.1 Experiments and Results
4.3 Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification
4.3.1 Design of the Type-1 Fuzzy Systems for Classification with Triangular Membership Functions
4.3.2 Design of the Type-1 FS for Classification with Trapezoidal Membership Functions
4.3.3 Design of the Type-1 FS for Classification with Gaussian Membership Functions
4.3.4 Design of the Interval Type-2 FS for Classification with Triangular Membership Functions
4.3.5 Design of the Interval Type-2 FS for Classification with Trapezoidal Membership Functions
4.3.6 Design of the Interval Type-2 FS for Classification with Gaussian Membership Functions
4.3.7 Fuzzy Rules for the Type-1 and Interval Type-2 FS with the Different Architectures
4.3.8 Knowledge Representation of the Optimized Type-1 and Interval Type-2 Fuzzy Systems
4.3.9 Knowledge Representation of Triangular, Trapezoidal and Gaussian Type-2 Membership Function for Interval Type-2 Fuzzy Systems
4.3.10 Results of This Work
4.3.11 Statistical Test
4.3.12 Discussion
4.4 Blood Pressure Load
4.4.1 Blood Pressure Load
4.4.2 Examples of a Monitoring Record with Blood Pressure Load
4.4.3 Optimization of Type-1 and Type-2 Fuzzy System for the Classification of Blood Pressure Load
4.4.4 Knowledge Representation of the Optimized Type-1 and Interval Type-2 Fuzzy Systems with Triangular Memberships Function
4.4.5 Input Variables for Triangular Type-1 Fuzzy System
4.4.6 Results
4.5 Classification of Blood Pressure Level and Blood Pressure Load Using Bio-Inspired Algorithms: Genetic Algorithm (GA) and Chicken Swarm Optimization (CSO)
4.5.1 Statistical Test
References
5 Conclusions of the Neuro Fuzzy Hybrid Model
Appendix
Index
