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001432381 001__ 1432381
001432381 003__ OCoLC
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001432381 019__ $$a1202441345
001432381 020__ $$a9783030604813$$q(electronic bk.)
001432381 020__ $$a3030604810$$q(electronic bk.)
001432381 020__ $$z3030604802
001432381 020__ $$z9783030604806
001432381 0247_ $$a10.1007/978-3-030-60481-3$$2doi
001432381 035__ $$aSP(OCoLC)1204139600
001432381 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dYDX$$dEBLCP$$dGW5XE$$dOCLCO$$dOCLCF$$dN$T$$dOCLCO$$dOCLCQ$$dOCLCO$$dOCLCQ
001432381 049__ $$aISEA
001432381 050_4 $$aR859.7.A78
001432381 08204 $$a610.285/63$$223
001432381 1001_ $$aMelin, Patricia,$$d1962-
001432381 24510 $$aNeuro fuzzy hybrid models for classification in medical diagnosis/$$cPatricia Melin, Juan Carlos Guzmán, German Prado-Arechiga.
001432381 260__ $$aCham :$$bSpringer,$$c2021.
001432381 300__ $$a1 online resource (109 pages)
001432381 336__ $$atext$$btxt$$2rdacontent
001432381 337__ $$acomputer$$bc$$2rdamedia
001432381 338__ $$aonline resource$$bcr$$2rdacarrier
001432381 4901_ $$aSpringerBriefs in Applied Sciences and Technology
001432381 504__ $$aIncludes bibliographical references and index.
001432381 5050_ $$aIntro -- 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
001432381 5058_ $$a3.1 General and Specific Neuro Fuzzy Hybrid Models -- 3.2 Creation of the Modular Neural Network -- 4 Study Cases to Test the Neuro Fuzzy Hybrid Model -- 4.1 Design of the Fuzzy Systems for Classification -- 4.1.1 Design of the First Fuzzy Classifier for the Classification of Blood Pressure Levels -- 4.1.2 Design of the Second Fuzzy Classifier for the Classification of Blood Pressure Levels -- 4.1.3 Design of the Third Fuzzy Classifier for the Classification of Blood Pressure Levels -- 4.1.4 The Optimization of the Fuzzy System Using a Genetic Algorithm (GA)
001432381 5058_ $$a4.1.5 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
001432381 5058_ $$a4.3.2 Design of the Type-1 FS for Classification with Trapezoidal Membership Functions -- 4.3.3 Design of the Type-1 FS for Classification with Gaussian Membership Functions -- 4.3.4 Design of the Interval Type-2 FS for Classification with Triangular Membership Functions -- 4.3.5 Design of the Interval Type-2 FS for Classification with Trapezoidal Membership Functions -- 4.3.6 Design of the Interval Type-2 FS for Classification with Gaussian Membership Functions -- 4.3.7 Fuzzy Rules for the Type-1 and Interval Type-2 FS with the Different Architectures
001432381 5058_ $$a4.3.8 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
001432381 506__ $$aAccess limited to authorized users.
001432381 520__ $$aThis 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.
001432381 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 25, 2021).
001432381 650_0 $$aArtificial intelligence$$xMedical applications.
001432381 650_0 $$aBlood pressure$$xMeasurement$$xData processing.
001432381 650_6 $$aIntelligence artificielle en médecine.
001432381 650_6 $$aPression artérielle$$xMesure$$xInformatique.
001432381 655_0 $$aElectronic books.
001432381 7001_ $$aGuzmán, Juan Carlos.
001432381 7001_ $$aPrado-Arechiga, German.
001432381 77608 $$iPrint version:$$aMelin, Patricia.$$tNeuro Fuzzy Hybrid Models for Classification in Medical Diagnosis.$$dCham : Springer International Publishing AG, ©2020$$z9783030604806
001432381 830_0 $$aSpringerBriefs in applied sciences and technology.
001432381 852__ $$bebk
001432381 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-60481-3$$zOnline Access$$91397441.1
001432381 909CO $$ooai:library.usi.edu:1432381$$pGLOBAL_SET
001432381 980__ $$aBIB
001432381 980__ $$aEBOOK
001432381 982__ $$aEbook
001432381 983__ $$aOnline
001432381 994__ $$a92$$bISE