This book provides an examination of applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles of various techniques. Soft Computing Techniques in Connected Healthcare Systems presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. The authors examine how connected healthcare is the essence of combining a practical operative procedure of interconnectedness of electronic health records, mHealth, clinical informatics, electronic data exchange, practice management solutions, and pharmacy management. The book focuses on different soft computing techniques, such as artificial neural networks (ANNs), fuzzy logic, genetic algorithms (GAs), Machine Learning (ML), and Natural Language Processing (NLP), which will enhance services in connected health systems, such as remote diagnosis and monitoring, medication monitoring devices, identifying and treating the underlying causes of disorders and diseases, improved access to specialists, and lower healthcare costs. The chapters also examine descriptive, predictive, and social network techniques and discuss analytical tools and the important role they play in enhancing the services to connected healthcare systems. Finally, the authors address real-time challenges with real-world case studies to enhance the comprehension of topics. This book is intended for under graduate and graduate students, researchers, and practicing professionals in the field of connected healthcare. It provides an overview for beginners while also addressing professionals in the industry on the importance of soft computing approaches in connected healthcare systems.
Author(s): Moolchand Sharma, Suman Deswal, Umesh Gupta
Publisher: CRC Press
Year: 2023
Language: English
Pages: 313
Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Contents
Editors’ Profile
Contributors
Preface
About the Book
Chapter 1 Automation in Healthcare Forecast and Outcome: A Case Study
Chapter 2 Optimizing Smartphone Addiction Questionnaires with Smartphone Application and Soft Computing: An Intelligent Smartphone Usage Behavior Assessment Model
Chapter 3 Artificial Neural Network Model for Automated Medical Diagnosis
Chapter 4 Analyzing of Heterogeneous Perceptions of a Mutually Dependent Health Ecosystem System Survey
Chapter 5 Intuitionistic Fuzzy-Based Technique Ordered Preference by Similarity to the Ideal Solution (TOPSIS) Method: An MCDM Approach for the Medical Decision Making of Diseases
Chapter 6 Design of a Heuristic IoT-Based Approach as a Solution to a Self-Aware Social Distancing Paradigm
Chapter 7 Combined 3D Mesh and Generative Adversarial Network–Based Improved Liver Segmentation in Computed Tomography Images
Chapter 8 Applying Privacy by Design to Connected Healthcare Ecosystems
Chapter 9 Next-Generation Platforms for Device Monitoring, Management, and Monetization for Healthcare
Chapter 10 Real-Time Classification and Hepatitis B Detection with Evolutionary Data Mining Approach
Chapter 11 Healthcare Transformation Using Soft Computing Approaches and IoT Protocols
Chapter 12 Automated Detection and Classification of Focal and Nonfocal EEG Signals Using Ensemble Empirical Mode Decomposition and ANN Classifier
Chapter 13 Challenges and Future Directions of Fuzzy System in Healthcare Systems: A Survey
Chapter 14 Perceptual Hashing Function for Medical Images: Overview, Challenges, and the Future
Chapter 15 Deploying Machine Learning Methods for Human Emotion Recognition
Chapter 16 Maternal Health Risk Prediction Model Using Artificial Neural Network
Index
