Computational Intelligence in Healthcare
Applications, Challenges, and Management
- 214 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Computational Intelligence in Healthcare
Applications, Challenges, and Management
About this book
Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. Artificial intelligent systems offer great improvement in healthcare systems by providing more intelligent and convenient solutions and services assisted by machine learning, wireless communications, data analytics, cognitive computing, and mobile computing. Modern health treatments are faced with the challenge of acquiring, analysing, and applying the large amount of knowledge necessary to solve complex problems. AI techniques are being effectively used in the field of healthcare systems by extracting the useful information from the vast amounts of data by applying human expertise and CI methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods which have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice.
This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with them. Contained in the book are state-of-the-art CI methods and other allied techniques used in healthcare systems as well as advances in different CI methods that confront the problem of effective data analysis and storage faced by healthcare institutions.
The objective of this book is to provide the latest research related to the healthcare sector to researchers and engineers with a platform encompassing state-of-the-art innovations, research and design, and the implementation of methodologies.
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Information
Table of contents
- Cover
- Half Title
- Series
- Title
- Copyright
- Contents
- Editors
- Contributors
- 1 Introduction to computational intelligence in healthcare: Applications, challenges, and management
- 2 Role of IoT and machine learning for e-healthcare management
- 3 Telemedical and remote healthcare monitoring using IoT and machine learning
- 4 Efficient ways for healthcare data management using data science and machine learning
- 5 A novel scheme to manage the e-healthcare system using cloud computing and the internet of things
- 6 Automating remote point-of-care ECG diagnostics via decentralized report routing algorithm
- 7 Evaluation of deep image embedders for healthcare informatics improvement using visualized performance metrics
- 8 A comparative analysis for analysing the performance of convolutional neural network versus other machine learning techniques to assess cardiovascular disease
- 9 A study of machine learning initiatives in the global healthcare sector using different case studies
- 10 Autism spectrum disorder diagnostic system using adaptive neuro fuzzy inference system
- 11 Detection of diabetic foot ulcer (DFU) with AlexNet and ResNet-101
- 12 A case study–based analysis on remote medical monitoring with AWS cloud and internet of things (IoT)
- Index