Role of Internet of Things and Machine Learning in Smart Healthcare
- 1st Edition, Volume 137 - February 1, 2025
- Imprint: Academic Press
- Editor: Suyel Namasudra
- Language: English
- Hardback ISBN:9 7 8 - 0 - 4 4 3 - 2 2 3 8 6 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 2 3 8 7 - 7
Role of Internet of Things and Machine Learning in Smart Healthcare, Volume 137 of the Advances in Computers series, presents detailed coverage of innovations in computer hardwa… Read more
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Request a sales quoteRole of Internet of Things and Machine Learning in Smart Healthcare, Volume 137 of the Advances in Computers series, presents detailed coverage of innovations in computer hardware, software, theory, design, and applications. Published since 1960, this series provides contributors with a medium to explore their subjects in greater depth and breadth than typical journal articles. Additionally, the book discusses the basic concepts of the Internet of Things (IoT) and Machine Learning (ML), along with their various applications in smart healthcare. It proposes novel techniques by integrating IoT, cloud computing, and ML algorithms to efficiently manage e-healthcare data and improve security. The volume also addresses research challenges and probable future directions in smart healthcare using IoT and ML, making it a comprehensive resource for researchers, practitioners, and students interested in advancing healthcare technologies.
- Provides in-depth surveys and tutorials on new computer technology, with this release focusing on IOT and Machine Learning in Smart Healthcare
- Presents well-known authors and researchers in the field
- Includes volumes that are devoted to single themes or subfields of computer science
Researchers in high performance computer areas, hardware manufacturers, educational programs in physics and scientific computation and in computer science
- Role of Internet of Things and Machine Learning in Smart Healthcare
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Chapter One Introducing the Internet of Things: Fundamentals, challenges, and applications
- Abstract
- Keywords
- 1 Introduction
- 2 Fundamentals of IoT
- 2.1 History of IoT
- 2.2 Components of IoT
- 2.3 Characteristics of IoT
- 2.4 Advantages of IoT
- 3 IoT architecture
- 4 Technologies of IoT
- 5 Key challenges of IoT
- 6 Applications of IoT
- 7 Conclusions
- References
- Chapter Two Introduction to machine learning
- Abstract
- Keywords
- 1 Introduction
- 2 Fundamental concepts
- 2.1 History of ML
- 2.2 Importance of ML
- 3 ML paradigms
- 3.1 Supervised learning
- 3.2 Unsupervised learning
- 3.3 Semisupervised learning
- 3.4 Reinforcement learning
- 4 Design of ML experiment
- 4.1 Model complexity and generalization
- 4.2 Selection of dataset
- 4.3 Randomization and cross validation
- 4.4 Performance metrics
- 5 Major issues in ML algorithms
- 5.1 Inadequate training data
- 5.2 Poor quality of data
- 5.3 Nonrepresentative training data
- 5.4 Overfitting
- 5.5 Underfitting
- 5.6 Getting bad recommendations
- 5.7 Lack of skilled resources
- 5.8 Customer segmentation
- 5.9 Process complexity of ML
- 5.10 Data bias
- 6 Applications of ML
- 7 Conclusions and future works
- References
- Chapter Three Revolutionizing patient care: The synergy of IoT and machine learning in smart healthcare
- Abstract
- Keywords
- 1 Introduction
- 2 Electronic health records
- 2.1 EHRs for prospective clinical study
- 2.2 Challenges of using EHRs for clinical trials
- 3 Internet of Medical Things
- 4 Applications of IoT in smart healthcare
- 4.1 IoT for doctors
- 4.2 IoT for hospitals
- 4.3 IoT for the pharmaceutical sector
- 4.4 IoT for patients
- 5 Applications of machine learning in Smart Healthcare
- 5.1 Prediction and diagnosis of diseases
- 5.2 Drug discovery
- 5.3 Personalized medicine
- 5.4 Medical imaging
- 5.5 Health record
- 5.6 Radiotherapy
- 5.7 Neurocritical care
- 5.8 Clinical trial and research
- 6 Disadvantages of using IoT and machine learning in healthcare
- 7 Conclusion
- References
- Chapter Four Security, privacy, and trust management of IoT and machine learning-based smart healthcare systems
- Abstract
- Keywords
- 1 Introduction
- 2 Role of CPS in healthcare
- 2.1 Model of CPS
- 2.2 Advantages of CPS
- 2.3 Challenges of CPS
- 2.4 Security mechanisms
- 2.5 Roles
- 3 IoT and ML-based smart healthcare system
- 3.1 Security and privacy issues in IoT and ML-based smart healthcare systems
- 3.2 Common security and privacy issues
- 4 Security attacks and solutions in smart healthcare systems
- 4.1 Attacks
- 4.2 Related work
- 5 Trust management in IoT and ML-based smart healthcare systems
- 6 Conclusions and future works
- References
- Chapter Five Machine learning-enabled IoT applications for smart healthcare monitoring systems
- Abstract
- Keywords
- 1 Introduction
- 1.1 Motivation
- 1.2 Problem statement
- 1.3 Contribution of the chapter
- 1.4 Organization of the chapter
- 2 Architecture of smart healthcare monitoring system
- 2.1 Overview of smart healthcare systems
- 2.2 Key components of the smart healthcare monitoring system
- 2.3 Need for IoT and ML integration in smart healthcare monitoring system
- 3 ML-based smart healthcare monitoring systems using IoT
- 3.1 Role of IoT in smart healthcare monitoring systems
- 3.2 Role of ML in smart healthcare monitoring systems
- 3.3 ML-based IoT integration system
- 4 ML-based remote healthcare monitoring using IoT
- 4.1 Key components and methodology
- 4.2 Benefits
- 4.3 Challenges
- 5 ML-based telemedicine systems using IoT
- 5.1 Key components and methodology
- 5.2 Benefits
- 5.3 Challenges
- 6 ML-based schemes for managing EHR using IoT
- 6.1 Remote patient monitoring
- 6.2 Predictive analytics for disease management
- 6.3 Healthcare resource optimization
- 6.4 Medication adherence monitoring
- 6.5 Clinical decision support systems
- 7 ML-based security techniques for improving security of EHR
- 7.1 Security challenges in healthcare data
- 7.2 ML-driven security solutions
- 7.3 Access control and authentication
- 7.4 Data encryption
- 8 ML-based models for medical big data using IoT
- 8.1 Dynamics of healthcare data
- 8.2 IoT data sources and big data collection
- 8.3 ML algorithms for medical insights
- 8.4 Real-time analytics and decision support
- 9 Discussion
- 9.1 Importance of this research to healthcare management
- 9.2 Future directions and research opportunities
- 10 Conclusions
- Acknowledgment
- References
- Chapter Six A smart healthcare system using IoT and machine learning
- Abstract
- Keywords
- 1 Introduction
- 2 Related works
- 3 Problem statements
- 3.1 System model
- 3.2 Design of the system model
- 4 Proposed scheme
- 4.1 Integrated data
- 4.2 Data preprocessing
- 4.3 Model design and training
- 4.4 Cloud-based model validation
- 5 Performance analysis
- 5.1 Experimental environment
- 5.2 Dataset description
- 5.3 Results and discussion
- 6 Conclusions
- References
- Chapter Seven Predicting and diagnosis of COVID-19 based on IoT and machine learning algorithm
- Abstract
- Keywords
- 1 Introduction
- 2 Literature reviews
- 3 Background studies
- 3.1 COVID-19
- 3.2 IoT devices
- 3.3 ML algorithm
- 4 Proposed architecture
- 5 Performance analysis
- 5.1 Experimental environment
- 5.2 Details of dataset
- 5.3 Results and discussion
- 6 Conclusions and future works
- References
- Chapter Eight A novel scheme for managing e-healthcare data using IoT and cloud computing
- Abstract
- Keywords
- 1 Introduction
- 2 Related works
- 3 Preliminary studies
- 3.1 Electronic health record
- 3.2 Internet of Things
- 3.3 Cloud computing
- 4 Problem statements
- 4.1 System model
- 4.2 System requirement
- 4.3 Design goals
- 5 Proposed methodology
- 5.1 Needs assessment and requirement analysis
- 5.2 Designing IoT infrastructure
- 5.3 Cloud infrastructure setup
- 5.4 Data collection and transmission
- 6 Performance analysis
- 6.1 Experimental environment
- 6.2 Results and discussion
- 7 Conclusions
- References
- Chapter Nine Improving security of e-healthcare data by using machine learning
- Abstract
- Keywords
- 1 Introduction
- 2 Literature review
- 2.1 Existing schemes used in real time health monitoring
- 2.2 Security threat model for smart healthcare system
- 3 Proposed work
- 3.1 Architecture
- 3.2 Smart contracts
- 4 Security analysis
- 5 Performance analysis
- 5.1 Experimental environment
- 5.2 Details of dataset
- 5.3 Results and discussion
- 6 Conclusions and future work
- References
- Chapter Ten Research challenges and future work directions in smart healthcare using IoT and machine learning
- Abstract
- Keywords
- 1 Introduction
- 2 Research challenges
- 2.1 Security
- 2.2 Scalability
- 2.3 Standards
- 2.4 Reliability
- 2.5 Data and information
- 2.6 Power consumption
- 2.7 End user
- 2.8 Organizational
- 2.9 Cost
- 3 Future work direction
- 4 Conclusions
- References
- Edition: 1
- Volume: 137
- Published: February 1, 2025
- No. of pages (Hardback): 396
- No. of pages (eBook): 310
- Imprint: Academic Press
- Language: English
- Hardback ISBN: 9780443223860
- eBook ISBN: 9780443223877
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Suyel Namasudra
Suyel Namasudra has received PhD from the National Institute of Technology Silchar, Assam, India. He was a Postdoctorate Fellow at the International University of La Rioja (UNIR), Spain. Currently, Dr. Namasudra is working as an Assistant Professor in the Department of Computer Science and Engineering at the National Institute of Technology Agartala, Tripura, India. Before joining the National
Institute of Technology Agartala, Dr. Namasudra was an Assistant Professor in the Department
of Computer Science and Engineering at the National Institute of Technology Patna, Bihar, India. His research interests include blockchain technology, cloud computing, IoT, AI, and DNA computing. Dr. Namasudra has edited 4 books, 5 patents, and 75 publications in conference proceedings, book chapters, and refereed journals like IEEE TII, IEEE T-ITS, IEEE TSC, IEEE TCSS, IEEE TCBB, ACM TOMM, ACM TOSN, ACM TALLIP, FGCS, CAEE, and many more. He has served as a Lead Guest Editor/Guest Editor in many reputed journals like ACM TOMM (ACM, IF: 3.144), MONE
(Springer, IF: 3.426), CAEE (Elsevier, IF: 3.818), CAIS (Springer, IF: 4.927), CMC (Tech Science Press, IF: 3.772), Sensors (MDPI, IF: 3.576), and many more. Dr. Namasudra is the Editor-in-Chief of the Cloud Computing and Data Science (ISSN: 2737-4092 (online)) journal, and he has participated in many international conferences as an organizer and session Chair. He is a member of IEEE, ACM,
and IEI. Dr. Namasudra has been featured in the list of the top 2% scientists in the world in 2021 and 2022, and his h-index is 29.
Affiliations and expertise
Assistant Professor, Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, IndiaRead Role of Internet of Things and Machine Learning in Smart Healthcare on ScienceDirect