Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics
Language: English
Published by Elsevier Science Publishing Co Inc, US, 2021
- Softcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
Condition: New
£ 204.03
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Add to basketItem description from seller
Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians.…
Seller Inventory # LU-9780128216330
- Title
- Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics
- Author
- Pradeep N
- Publisher
- Elsevier Science Publishing Co Inc, US
- Publication year
- 2021
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 0128216336
- ISBN 13
- 9780128216330
- Item weight
- 770 grams
Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians.
- Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies
- Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics
- Unique case study approach provides readers with insights for practical clinical implementation
"Synopsis" may belong to another edition of this title.
About the Author
Sandeep Kautish, PhD is Professor and Director at Apex Institute of Technology (AIT-CSE), Chandigarh University, Punjab India and an academician by choice and has more than 20 years of full-time experience in teaching and research. He has been associated with Asia Pacific University Malaysia for over five years at their TNE site at Kathmandu Nepal in the capacity of Director-Academics. He earned his doctorate degree in Computer Science on Intelligent Systems in Social Networks. He has over 100 publications and his research works have been published in highly reputed journals, i.e., IEEE Transaction of Industrial Informatics, IEEE Access, and Multimedia Tools and Applications, etc. Dr. Kautish has edited 24 books with leading publishers, i.e., Elsevier, Springer, Emerald, and IGI Global, and is an editorial member/reviewer of various reputed journals. His research interests include healthcare analytics, business analytics, machine learning, data mining, and information systems.
Dr. Sheng-Lung Peng is a full Professor in the Department of Computer Science and Information Engineering at
National Dong Hwa University, Taiwan. He received his PhD degree in Computer Science and Information
Engineering from the National Tsing Hua University, Taiwan. His research interests are in designing and analyzing
algorithms for Bioinformatics, Combinatorics, Data Mining, and Networks. Dr. Peng has edited several special
issues for journals, such as Soft Computing, Journal of Internet Technology, and MDPI Algorithms. He is also a
reviewer for many journals such as IEEE Access and Transactions on Emerging Topics in Computing, IEEE/ACM
Transactions on Networking, Theoretical Computer Science, Journal of Computer and System Sciences, Journal
of Combinatorial Optimization, Journal of Modelling in Management, Soft Computing, Information Processing
Letters, Discrete Mathematics, Discrete Applied Mathematics, and Graph Theory. Dr. Peng is currently the Dean
of the Library and Information Services Office of NDHU, an honorary Professor of Beijing Information Science
and Technology University, China, and a visiting Professor at Ningxia Institute of Science and Technology, China.
He is the regional director of the ACM-ICPC Contest Council for Taiwan, a director of the Institute of Information
and Computing Machinery (IICM), a director of the Information Service Association of Chinese Colleges and of
the Taiwan Association of Cloud Computing (TACC). He is also a supervisor of the Chinese Information Literacy
Association, Chairman of the Association of Algorithms and Computation Theory (AACT) and Chairman of the
Interlibrary Cooperation Association in Taiwan.
"About the title" may belong to another edition of this title.
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