Machine Learning, Big Data, and IoT for Medical Informatics

1st Edition - June 13, 2021
Editors: Pardeep Kumar, Fatos Xhafa, Yugal Kumar, Mohamed A. Tawhid
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 1 7 7 7 - 1
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 1 7 8 1 - 8

Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning… Read more

Purchase options

SUSTAINABLE DEVELOPMENT

Innovate. Sustain. Transform.

Save up to 30% on top Physical Sciences & Engineering titles!

Explore now

Physical science & engineering for sustainable development book covers

Institutional subscription on ScienceDirect

Request a sales quote

Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics.

In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data.

This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Outline of the book and chapter synopses
Special acknowledgments
Chapter 1: Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques
Abstract
1: Introduction: Predictive analytics for medical informatics
2: Background
3: Techniques for machine learning
4: Applications
5: Experimental results
6: Conclusion: Machine learning for computational medicine
Chapter 2: Geolocation-aware IoT and cloud-fog-based solutions for healthcare
Abstract
1: Introduction
2: Related work
3: Proposed framework
4: Performance evaluation
5: Conclusion and future work
Chapter 3: Machine learning vulnerability in medical imaging
Abstract
Acknowledgment
1: Introduction
2: Computer vision
3: Adversarial computer vision
4: Methods to produce adversarial examples
5: Adversarial attacks
6: Adversarial defensive methods
7: Adversarial computer vision in medical imaging
8: Adversarial examples: How to generate?
9: Conclusion
Chapter 4: Skull stripping and tumor detection using 3D U-Net
Abstract
1: Introduction
2: Overview of U-net architecture
3: Materials and methods
4: Results
5: Conclusion
Chapter 5: Cross color dominant deep autoencoder for quality enhancement of laparoscopic video: A hybrid deep learning and range-domain filtering-based approach
Abstract
Acknowledgments
1: Introduction
2: Range-domain filtering
3: Cross color dominant deep autoencoder (C2D2A) leveraging color spareness and saliency
4: Experimental results
5: Conclusion
Chapter 6: Estimating the respiratory rate from ECG and PPG using machine learning techniques
Abstract
Acknowledgments
1: Introduction
2: Related work
3: Methods
4: Experimental results
5: Discussion and conclusion
Chapter 7: Machine learning-enabled Internet of Things for medical informatics
Abstract
1: Introduction
2: Applications and challenges of H-IoT
3: Machine learning
4: Future research directions
5: Conclusion
Chapter 8: Edge detection-based segmentation for detecting skin lesions
Abstract
1: Introduction
2: Previous works
3: Materials and methods
4: Proposed method
5: Experiment and results
6: Conclusion
Chapter 9: A review of deep learning approaches in glove-based gesture classification
Abstract
1: Introduction
2: Data gloves
3: Gesture taxonomies
4: Gesture classification
5: Discussion and future trends
6: Conclusion
Chapter 10: An ensemble approach for evaluating the cognitive performance of human population at high altitude
Abstract
Acknowledgment
1: Introduction
2: Methodology
3: Results and discussion
4: Future opportunities
5: Conclusions
Chapter 11: Machine learning in expert systems for disease diagnostics in human healthcare
Abstract
Acknowledgment
1: Introduction
2: Types of expert systems
3: Components of an expert system
4: Techniques used in expert systems of medical diagnosis
5: Existing expert systems
6: Case studies
7: Significance and novelty of expert systems
8: Limitations of expert systems
9: Conclusion
Chapter 12: An entropy-based hybrid feature selection approach for medical datasets
Abstract
1: Introduction
2: Background of the present research
3: Methodology
4: Experiment and experimental results
5: Discussion
6: Conclusions and future works
Conflict of interest
Appendix A
Chapter 13: Machine learning for optimizing healthcare resources
Abstract
1: Introduction
2: The state of the art
3: Machine learning for health data analysis
4: Feature selection techniques
5: Machine learning classifiers
6: Case studies
7: Case study 2: COVID-19 data analysis
8: Summary and future directions
Chapter 14: Interpretable semisupervised classifier for predicting cancer stages
Abstract
Acknowledgments
1: Introduction
2: Self-labeling gray box
3: Data preparation
4: Experiments and discussion
5: Conclusions
Chapter 15: Applications of blockchain technology in smart healthcare: An overview
Abstract
1: Introduction
2: Blockchain overview
3: Proposed healthcare monitoring framework
4: Blockchain-enabled healthcare applications
5: Potential challenges
6: Concluding remarks
Chapter 16: Prediction of leukemia by classification and clustering techniques
Abstract
1: Introduction
2: Motivation
3: Literature review
4: Description of proposed system
5: Simulation results and discussion
6: Conclusion and future directions
Chapter 17: Performance evaluation of fractal features toward seizure detection from electroencephalogram signals
Abstract
Acknowledgments
1: Introduction
2: Fractal dimension
3: Dataset
4: Experiments
5: Results and discussion
6: Conclusion
Chapter 18: Integer period discrete Fourier transform-based algorithm for the identification of tandem repeats in the DNA sequences
Abstract
1: Introduction
2: Related work
3: Algorithm for detection of TRs
4: Performance analysis of the proposed algorithm
5: Conclusion
Chapter 19: A blockchain solution for the privacy of patients’ medical data
Abstract
1: Introduction
2: Stakeholders of healthcare industry
3: Data protection laws for healthcare industry
4: Medical data management
5: Issues and challenges of healthcare industry
6: Blockchain technology
7: Blockchain applications in healthcare
8: Blockchain-based framework for privacy protection of patient’s data
9: Conclusion
Chapter 20: A novel approach for securing e-health application in a cloud environment
Abstract
1: Introduction
2: Motivation
3: Proposed system
4: Conclusion
Chapter 21: An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm
Abstract
1: Introduction
2: Data analytics
3: Machine learning
4: Approaching ensemble learning
5: Understanding bagging
6: Exploring boosting
7: Discovering stacking
8: Processing drug discovery with machine learning
9: Conclusion
Chapter 22: A review of deep learning models for medical diagnosis
Abstract
1: Motivation
2: Introduction
3: MRI Segmentation
4: Deep learning architectures used in diagnostic brain tumor analysis
5: Deep learning tools applied to MRI images
6: Proposed framework
7: Conclusion and outlook
8: Future directions
Chapter 23: Machine learning in precision medicine
Abstract
1: Precision medicine
2: Machine learning
3: Machine learning in precision medicine
4: Future opportunities
5: Conclusions
Index

Pardeep Kumar

Dr. Pardeep Kumar is currently working as an Associate Professor in the Department of Computer Science and Engineering at Jaypee University of Information Technology, Waknaghat, Solan, India, and has more than 14 years of experience in academia. Previously, he was associated with Mody University of Technology and Science, Laxmangarh, Sikar, Rajasthan. He completed his PhD in Computer Science and Engineering at Uttarakhand Technical University, Dehradun. He obtained his MTech in Computer Science and Engineering from Guru Jambheshwar University of Science and Technology, Hisar, Haryana, and his BTech in Information Technology from Kurukshetra University, Kurukshetra, Haryana. Dr. Kumar has been appointed as Associate Editor of the IEEE Access journal, as well as Guest Editor of several SCI and Scopus Index journals of international repute.

Affiliations and expertise

Associate Professor, Department of Computer Science and Engineering, Jaypee University of Information Technology (JUIT), Solan, Himachal Pradesh, India

Fatos Xhafa

Fatos Xhafa, PhD in Computer Science, is Full Professor at the Technical University of Catalonia (UPC), Barcelona, Spain. He has held various tenured and visiting professorship positions. He was a Visiting Professor at the University of Surrey, UK (2019/2020), Visiting Professor at the Birkbeck College, University of London, UK (2009/2010) and a Research Associate at Drexel University, Philadelphia, USA (2004/2005). He was a Distinguished Guest Professor at Hubei University of Technology, China, for the duration of three years (2016-2019). Prof. Xhafa has widely published in peer reviewed international journals, conferences/workshops, book chapters, edited books and proceedings in the field (H-index 55). He has been awarded teaching and research merits by the Spanish Ministry of Science and Education, by IEEE conferences and best paper awards. Prof. Xhafa has an extensive editorial service. He is founder and Editor-In-Chief of Internet of Things - Journal - Elsevier (Scopus and Clarivate WoS Science Citation Index) and of International Journal of Grid and Utility Computing, (Emerging Sources Citation Index), and AE/EB Member of several indexed Int'l Journals. Prof. Xhafa is a member of IEEE Communications Society, IEEE Systems, Man & Cybernetics Society and Founder Member of Emerging Technical Subcommittee of Internet of Things. His research interests include IoT and Cloud-to-thing continuum computing, massive data processing and collective intelligence, optimization, security and trustworthy computing and machine learning, among others. He can be reached at [email protected]. Please visit also http://www.cs.upc.edu/~fatos/ and at http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos

Affiliations and expertise

Full Professor of Computer Science, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

Yugal Kumar

Dr. Yugal Kumar is currently working as an Assistant Professor (senior grade) in the Department of Computer Science and Engineering at Jaypee University of Information Technology, Waknaghat, Solan, India. He completed his PhD in Computer Science and Engineering at the Birla institute of Technology, Mesra, Ranchi, and has now more than 14 years of teaching and research experience. He has presented and published more than 80 research papers, as well as participated in various national and international conferences. He is serving as a member of the editorial review board of various journals including PLOS ONE, Journal of Advanced Computational Intelligence and Intelligent Informatics, and Journal of Information Processing System.

Affiliations and expertise

Assistant Professor, Department of Computer Science and Engineering, Jaypee University of Information Technology (JUIT), Solan, Himachal Pradesh, India

Mohamed A. Tawhid

Mohamed A. Tawhid earned his PhD in Applied Mathematics from the University of Maryland Baltimore County, Maryland, United States. From 2000 to 2002, he was a postdoctoral fellow at the Faculty of Management, McGill University, Montreal, Quebec, Canada. Currently, he is a Professor at Thompson Rivers University, Kamloops, British Columbia, Canada. He has published more than 75 peer-reviewed research papers, 13 book chapters and edited four special issues in international journals. He has also co-authored a book published by Springer. His research has been funded by Natural Sciences and Engineering Research Council (NSERC) grants. Moreover, he has served on several journals' editorial boards and worked on several industrial projects in Canada.

Affiliations and expertise

Professor, Department of Mathematics and Statistics, Thompson Rivers University, Kamloops, British Columbia, Canada

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Machine Learning, Big Data, and IoT for Medical Informatics

Purchase options

Innovate. Sustain. Transform.

Institutional subscription on ScienceDirect

Pardeep Kumar

Fatos Xhafa

Yugal Kumar

Mohamed A. Tawhid

Related books