Computation Intelligence (CI) paradigms, including artificial neural networks, fuzzy systems, evolutionary computing techniques, and intelligent agents, form the basis of making clinical decisions. This book explains different aspects of the current research on CI technologies applied in the field of medical diagnosis. It discusses critical issues related to medical diagnosis, like uncertainties in the medical domain, problems in the medical data, especially dealing with time-stamped data, and knowledge acquisition.
The present book includes basic as well as hybrid CI techniques that have been used in recent years so as to know the current trends in medical diagnosis domain. It also presents the merits and demerits of different techniques in general as well as application-specifc context. The presented book discusses some critical issues related to medical diagnosis, like uncertainties in the medical domain, problems in medical data, especially dealing with time-stamped (temporal) data, and knowledge acquisition. In addition, this book also discusses the features of good CI techniques in medical diagnosis. It is a collection of chapters that covers a rich and diverse variety of computer-based CI techniques, all involving some aspect of Computational Intelligence, but each one taking a somewhat-pragmatic view.
Features:
Introduces recent applications of new Computation Intelligence technologies focusing on medical diagnosis issues.
Reviews multidisciplinary research in health care, like data mining, medical imaging, pattern recognition, and so forth.
Explores intelligent systems and applications of learning in health-care challenges, along with the representation and reasoning of clinical uncertainty.
Addresses problems resulting from automated data collection in modern hospitals, with possible solutions to support medical decision-making systems.
Discusses current and emerging intelligent systems with respect to evolutionary computation and its applications in the medical domain.
This book is aimed at researchers, professionals, and graduate students in Computation Intelligence, signal processing, imaging, Artificial Intelligence, and data analytics.
Author(s): Sitendra Tamrakar, Shruti Bhargava Choubey, Abhishek Choubey
Publisher: CRC Press
Year: 2023
Language: English
Pages: 286
Cover
Half Title
Series
Title
Copyright
Dedication
Table of Contents
About the Editors
List of Contributors
Preface
Chapter 1 Prediction of Diseases Using Machine Learning Techniques
Chapter 2 A Novel Virtual Medicinal Care Model for Remote Treatments
Chapter 3 Artificial Intelligence in Future Telepsychiatry and Psychotherapy for E-Mental Health Revolution
Chapter 4 Optimized Convolutional Neural Network for Classification of Tumors from MR Brain Images
Chapter 5 Predictive Modeling of Epidemic Diseases Based on Vector-Borne Diseases Using Artificial Intelligence Techniques
Chapter 6 Hybrid Neural Network-Based Fuzzy Inference System Combined with Machine Learning to Detect and Segment Kidney Tumor
Chapter 7 Classification of Breast Tumor from Histopathological Images with Transfer Learning
Chapter 8 Performance of IoT-Enabled Devices in Remote Health Monitoring Applications
Chapter 9 Applying Machine Learning Logistic Regression Model for Predicting Diabetes in Women
Chapter 10 Compressive Sensing-Based Medical Imaging Techniques to Detect the Type of Pneumonia in Lungs
Chapter 11 Electroencephalogram (EEG) Signal Denoising Using Optimized Wavelet Transform (WT): A Study
Chapter 12 Predicting Diabetes in Women by Applying the Support Vector Machine (SVM) Model
Chapter 13 Data Mining Approaches on EHR System: A Survey
Chapter 14 Chest Tumor Identification in Mammograms by Selected Features Employing SVM
Chapter 15 A Novel Optimum Clustering Method Using Variant of NOA
Chapter 16 Role of Artificial Intelligence and Neural Network in the Health-Care Sector: An Important Guide for Health Prominence
Index
