Applications of Big Data in Healthcare
Theory and Practice
- 1st Edition - March 10, 2021
- Imprint: Academic Press
- Editors: Ashish Khanna, Deepak Gupta, Nilanjan Dey
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 0 2 0 3 - 6
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 0 4 5 1 - 1
Applications of Big Data in Healthcare: Theory and Practice begins with the basics of Big Data analysis and introduces the tools, processes and procedures associated with Big Data… Read more
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Request a sales quoteApplications of Big Data in Healthcare: Theory and Practice begins with the basics of Big Data analysis and introduces the tools, processes and procedures associated with Big Data analytics. The book unites healthcare with Big Data analysis and uses the advantages of the latter to solve the problems faced by the former. The authors present the challenges faced by the healthcare industry, including capturing, storing, searching, sharing and analyzing data. This book illustrates the challenges in the applications of Big Data and suggests ways to overcome them, with a primary emphasis on data repositories, challenges, and concepts for data scientists, engineers and clinicians.
The applications of Big Data have grown tremendously within the past few years and its growth can not only be attributed to its competence to handle large data streams but also to its abilities to find insights from complex, noisy, heterogeneous, longitudinal and voluminous data. The main objectives of Big Data in the healthcare sector is to come up with ways to provide personalized healthcare to patients by taking into account the enormous amounts of already existing data.
- Provides case studies that illustrate the business processes underlying the use of big data and deep learning health analytics to improve health care delivery
- Supplies readers with a foundation for further specialized study in clinical analysis and data management
- Includes links to websites, videos, articles and other online content to expand and support the primary learning objectives for each major section of the book
Computer/data scientists, biomedical engineers, researchers and software engineers in the areas of intelligent data analysis, deep learning, big data, and intelligent systems. Research scientists and practitioners in medical and biological sciences
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- About the authors
- Preface
- Objective of the book
- Organization of the book
- 1. Big Data classification: techniques and tools
- Abstract
- 1.1 Introduction
- 1.2 Big Data classification
- 1.3 Big Data classification techniques
- 1.4 Big Data classification tools and platforms
- 1.5 Conclusion
- References
- 2. Big Data Analytics for healthcare: theory and applications
- Abstract
- 2.1 Introduction to Big Data
- 2.2 Big Data Analytics
- 2.3 Big Data in healthcare sector
- 2.4 Medical imaging
- 2.5 Methodology
- 2.6 Big Data Analytics: platforms and tools
- 2.7 Opportunities for Big Data in healthcare
- 2.8 Challenges to Big Data Analytics in healthcare
- 2.9 Applications of Big Data in healthcare industry
- 2.10 Future of Big Data in healthcare
- References
- 3. Application of tools and techniques of Big data analytics for healthcare system
- Abstract
- 3.1 Introduction
- 3.2 Need and past work
- 3.3 Methods of application
- 3.4 Result domains
- 3.5 Discussion
- 3.6 Conclusion
- References
- 4. Healthcare and medical Big Data analytics
- Abstract
- 4.1 Introduction
- 4.2 Medical and healthcare Big Data
- 4.3 Big Data Analytics
- 4.4 Healthcare and medical data coding and taxonomy
- 4.5 Medical and healthcare data interchange standards
- 4.6 Framework for healthcare information system based on Big Data
- 4.7 Big Data security, privacy, and governance
- 4.8 Discussion and further work
- References
- 5. Big Data analytics in medical imaging
- Abstract
- 5.1 Introduction
- 5.2 Big Data analytics in medical imaging
- 5.3 Artificial intelligence for analytics of medical images
- 5.4 Tools and frameworks
- 5.5 Conclusion
- References
- 6. Big Data analytics and artificial intelligence in mental healthcare
- Abstract
- 6.1 Introduction
- 6.2 What makes mental healthcare complex?
- 6.3 Opportunities and limitations for artificial intelligence and big data in mental health
- 6.4 Conclusions
- Acknowledgments
- References
- 7. Big Data based breast cancer prediction using kernel support vector machine with the Gray Wolf Optimization algorithm
- Abstract
- 7.1 Introduction
- 7.2 Literature survey
- 7.3 Proposed methodology
- 7.4 Result and discussion
- 7.5 Conclusion
- References
- 8. Big Data based medical data classification using oppositional Gray Wolf Optimization with kernel ridge regression
- Abstract
- 8.1 Introduction
- 8.2 Literature survey
- 8.3 Proposed methodology
- 8.4 Result and discussion
- 8.5 Conclusion
- References
- 9. An analytical hierarchical process evaluation on parameters Apps-based Data Analytics for healthcare services
- Abstract
- 9.1 Introduction
- 9.2 Review of literature
- 9.3 Research methodology
- 9.4 Proposed analytical hierarchy processing model of successful healthcare
- 9.5 Conclusion
- Appendix 1
- References
- 10. Firefly—Binary Cuckoo Search Technique based heart disease prediction in Big Data Analytics
- Abstract
- 10.1 Introduction
- 10.2 Literature survey
- 10.3 Proposed methodology
- 10.4 Result and discussion
- 10.5 Conclusion
- References
- Further reading
- 11. Hybrid technique for heart diseases diagnosis based on convolution neural network and long short-term memory
- Abstract
- 11.1 Introduction
- 11.2 Literature review
- 11.3 The proposed technique
- 11.4 Experimental results and discussion
- 11.5 Results analysis and discussion
- 11.6 Conclusion
- References
- Further reading
- Index
- Edition: 1
- Published: March 10, 2021
- No. of pages (Paperback): 310
- No. of pages (eBook): 310
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128202036
- eBook ISBN: 9780128204511
AK
Ashish Khanna
Dr. Ashish Khanna has 16 years of expertise in teaching, entrepreneurship, and research and development. He received his PhD from the National Institute of Technology, Kurukshetra, India, and completed a post-doc degree at the National Institute of Telecommunications (Inatel), Brazil. He has published around 40 SCI-indexed papers in 'IEEE Transactions', and in other reputed journals by Springer, Elsevier, and Wiley, with a cumulative impact factor of above 100. He has published around 90 research articles in top SCI/Scopus journals, conferences, and book chapters. He is co-author or editor of numerous books, including 'Advanced Computational Techniques for Virtual Reality in Healthcare' (Springer), 'Intelligent Data Analysis: From Data Gathering to Data Comprehension' (Wiley), and 'Hybrid Computational Intelligence: Challenges and Applications' (Elsevier). His research interests include distributed systems, MANET, FANET, VANET, Internet of Things, and machine learning. He is one of the founders of Bhavya Publications and the Universal Innovator Lab, which is actively involved in research, innovation, conferences, start-up funding events, and workshops. He is currently working at the Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India, and is also a Visiting Professor at the University of Valladolid, Spain.
Affiliations and expertise
Sr. Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology (MAIT), New Delhi, IndiaDG
Deepak Gupta
Dr. Deepak Gupta is an Assistant Professor in the Department of Computer Science and Engineering at Maharaja Agrasen Institute
of Technology, Guru Gobind Singh Indraprastha University, India. He obtained his PhD from Dr. APJ Abdul Kalam Technical
University. He is a post doc research fellow in the Internet of Things research lab at Inatel, Brazil. He has been guest editor for 10
special journal issues, including ASoC (Elsevier), NCAA (Springer), Sensors (MPDI) and CAEE (Elsevier). He is Editor-in-Chief
of OA Journal - Computers and Associate Editor of Journal of Computational and Theoretical Nanoscience.
Affiliations and expertise
Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Guru Gobind Singh Indraprastha University, IndiaND
Nilanjan Dey
Nilanjan Dey (Senior Member, IEEE) received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Associate Professor with the Techno International New Town, Kolkata and a visiting fellow of the University of Reading, UK. He has authored over 300 research articles in peer-reviewed journals and international conferences and 40 authored books. His research interests include medical imaging and machine learning. Moreover, he actively participates in program and organizing committees for prestigious international conferences, including World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), International Congress on Information and Communication Technology (ICICT), International Conference on Information and Communications Technology for Sustainable Development (ICT4SD) etc.
He is also the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society and series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research from Springer Nature and Advances in Ubiquitous Sensing Applications for Healthcare from Elsevier etc. Furthermore, he was an Editorial Board Member Complex & Intelligence Systems, Springer, Applied Soft Computing, Elsevier and he is an International Journal of Information Technology, Springer, International Journal of Information and Decision Sciences etc. He is a Fellow of IETE and member of IE, ISOC etc.
Affiliations and expertise
Associate Professor, Department of Computer Science and Engineering, Techno International New Town, Kolkata, India; Visiting Fellow , University of Reading, UKRead Applications of Big Data in Healthcare on ScienceDirect