LEADER 06976nam 22006735 450 001 9910523784203321 005 20251113192454.0 010 $a3-030-82219-2 024 7 $a10.1007/978-3-030-82219-4 035 $a(CKB)4100000011995869 035 $a(MiAaPQ)EBC6694620 035 $a(Au-PeEL)EBL6694620 035 $a(PPN)257352228 035 $a(OCoLC)1265460630 035 $a(DE-He213)978-3-030-82219-4 035 $a(EXLCZ)994100000011995869 100 $a20210806d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNature-inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis /$fby Patricia Melin, Ivette Miramontes, German Prado Arechiga 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (134 pages) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3712 311 08$a3-030-82218-4 327 $aIntro -- Preface -- Contents -- 1 Introduction to Soft Computing Applied in Medicine -- References -- 2 Theory of Soft Computing and Medical Terms -- 2.1 Hybrid Systems -- 2.1.1 Artificial Neural Networks -- 2.1.2 Type-1 Fuzzy Systems -- 2.1.3 Type-2 Fuzzy Logic -- 2.1.4 Optimization -- 2.1.5 CSO -- 2.1.6 CSA -- 2.1.7 FPA -- 2.1.8 BSA -- 2.2 Blood Pressure -- 2.2.1 Hypertension -- 2.2.2 Heart Rate -- 2.2.3 Nocturnal Blood Pressure Profile -- 2.2.4 Ambulatory Blood Pressure Monitoring -- 2.2.5 Framingham Heart Study -- 2.2.6 Cardiovascular Risk -- References -- 3 Proposed Model to Obtain the Medical Diagnosis -- 3.1 Neuro-Fuzzy Hybrid Model -- 3.2 IT2FS for Classification of Heart Rate Level -- 3.3 IT2FS for Classification of Nocturnal Bloor Pressure Profile -- 4 Study Cases to Test the Optimization Performed in the Hybrid Model -- 4.1 Optimization of the Fuzzy System to Provide the Correct Classification of the Nocturnal Blood Pressure Profile -- 4.1.1 Design of a Fuzzy System for Classification of Nocturnal Blood Pressure Profile -- 4.1.2 Experimentation and Results -- 4.1.3 Statistical Test -- 4.2 Fuzzy System Optimization to Obtain the Heart Rate Level -- 4.2.1 Proposed Method for Optimization of the Heart Rate Fuzzy Classifier -- 4.2.2 Type-1 Fuzzy System Optimization Using the BSA -- 4.2.3 Design and Optimization of the IT2FS -- 4.2.4 Results Obtained from Optimizing the Heart Rate Fuzzy System -- 4.3 Optimization of the Modular Neural Network to Obtain the Trend of the Blood Pressure -- 4.3.1 Proposed Method for Optimization of the Modular Neural Network -- 4.3.2 Results of the Optimization Made to the Modular Neural Network -- 4.4 Optimization of the Artificial Neural Network Used to Obtain the Risk of Developing Hypertension. 327 $a4.4.1 Proposed Method for the Optimization of the Monolithic Neural Network Used to Obtain the Risk of Developing Hypertension -- 4.4.2 Results Obtained from the Optimization -- 4.4.3 Z-test of FPA and ALO Versus Simple Enumeration Method -- 4.5 Optimization of the Modular Neural Network to Obtain the Risk of Developing a Cardiovascular Event -- 4.5.1 Proposed Method for Optimizing the Modular Network for the Risk of Developing a Cardiovascular Event -- 4.5.2 Experimentation and Results of the Optimization -- 4.6 Fuzzy Bird Swarm Algorithm -- 4.6.1 Proposed Method for the Dynamic Parameter Adjustment -- 4.6.2 Experiments and Results -- 4.6.3 Results -- 4.6.4 Statistical Test -- References -- 5 Conclusions of the Hybrid Medical Model -- Appendix A Knowledge Representation -- Type-1 Fuzzy System Knowledge Representation for Heart Rate Classification -- Inputs Variables -- Output Variable -- IT2FS Knowledge Representation Using Gaussian Membership Functions -- Inputs Variables -- Output Variable -- Knowledge Representation of the Fuzzy Classifier to Obtain the Nocturnal Blood Pressure Profile -- Inputs Variables -- Appendix B Graphical User Interface -- Index. 330 $aThis book describes the utilization of different soft computing techniques and their optimization for providing an accurate and efficient medical diagnosis. The proposed method provides a precise and timely diagnosis of the risk that a person has to develop a particular disease, but it can be adaptable to provide the diagnosis of different diseases. This book reflects the experimentation that was carried out, based on the different optimizations using bio-inspired algorithms (such as bird swarm algorithm, flower pollination algorithms, and others). In particular, the optimizations were carried out to design the fuzzy classifiers of the nocturnal blood pressure profile and heart rate level. In addition, to obtain the architecture that provides the best result, the neurons and the number of neurons per layers of the artificial neural networks used in the model are optimized. Furthermore, different tests were carried out with the complete optimized model. Another work that is presented in this book is the dynamic parameter adaptation of the bird swarm algorithm using fuzzy inference systems, with the aim of improving its performance. For this, different experiments are carried out, where mathematical functions and a monolithic neural network are optimized to compare the results obtained with the original algorithm. The book will be of interest for graduate students of engineering and medicine, as well as researchers and professors aiming at proposing and developing new intelligent models for medical diagnosis. In addition, it also will be of interest for people working on metaheuristic algorithms and their applications on medicine. 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3712 606 $aComputational intelligence 606 $aBiomedical engineering 606 $aEngineering$xData processing 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aBiomedical Engineering and Bioengineering 606 $aData Engineering 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aBiomedical engineering. 615 0$aEngineering$xData processing. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aBiomedical Engineering and Bioengineering. 615 24$aData Engineering. 615 24$aArtificial Intelligence. 676 $a610.1511322 700 $aMelin$b Patricia$0762263 702 $aMiramontes$b Ivette 702 $aPrado-Arechiga$b German 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910523784203321 996 $aNature-Inspired Optimization of Type-2 Fuzzy Neural Hybrid Models for Classification in Medical Diagnosis$92591574 997 $aUNINA