Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics
- 1st Edition - June 10, 2021
- Imprint: Academic Press
- Editors: Pradeep N, Sandeep Kautish, Sheng-Lung Peng
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 1 6 3 3 - 0
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 2 0 4 4 - 3
Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Variou… Read more
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Request a sales quoteDemystifying 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
Academic Researchers in Biomedical Engineering, Computer Science, and researchers in machine learning, deep learning, and Big Data. Clinicians and researchers in various medical research and clinical settings
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Contributors
- Editors biography
- Foreword
- Preface
- Overview
- Section 1: Big data in healthcare analytics
- Chapter 1: Foundations of healthcare informatics
- Abstract
- 1.1: Introduction
- 1.2: Goals of healthcare informatics
- 1.3: Focus of healthcare informatics
- 1.4: Applications of healthcare informatics
- 1.5: Medical information
- 1.6: Clinical decision support systems
- 1.7: Developing clinical decision support systems
- 1.8: Healthcare information management
- 1.9: Control flow
- 1.10: Other perspectives
- 1.11: Conclusion
- Chapter 2: Smart healthcare systems using big data
- Abstract
- 2.1: Introduction
- 2.2: Big data analytics in healthcare
- 2.3: Related work
- 2.4: Big data for biomedicine
- 2.5: Proposed solutions for smart healthcare model
- 2.6: Role of sensor technology for eHealth
- 2.7: Major applications and challenges
- 2.8: Conclusion and future scope
- Chapter 3: Big data-based frameworks for healthcare systems
- Abstract
- 3.1: Introduction
- 3.2: The role of big data in healthcare systems and industry
- 3.3: Big data frameworks for healthcare systems
- 3.4: Overview of big data techniques and technologies supporting healthcare systems
- 3.5: Overview of big data platform and tools for healthcare systems
- 3.6: Proposed big data-based conceptual framework for healthcare systems
- 3.7: Conclusion
- Chapter 4: Predictive analysis and modeling in healthcare systems
- Abstract
- 4.1: Introduction
- 4.2: Process configuration and modeling in healthcare systems
- 4.3: Basic techniques of process modeling and prediction
- 4.4: Event log
- 4.5: Control perspective of hospital process using various modeling notations
- 4.6: Predictive modeling control flow of a process using fuzzy miner
- 4.7: Open research problems
- 4.8: Conclusion
- Chapter 5: Challenges and opportunities of big data integration in patient-centric healthcare analytics using mobile networks
- Abstract
- 5.1: Introduction
- 5.2: Elderly health monitoring using big data
- 5.3: Personalized monitoring and support platform (MONISAN)
- 5.4: Patient-centric healthcare provider using big data
- 5.5: Patient-centric optimization model
- 5.6: The WSRMAX approach-based MILP formulation
- 5.7: MILP formulation-probability fairness approach
- 5.8: Heuristic approach
- 5.9: Results and discussion
- 5.10: Future directions
- 5.11: Conclusion
- Chapter 6: Emergence of decision support systems in healthcare
- Abstract
- 6.1: Introduction
- 6.2: Transformation in healthcare systems
- 6.3: CDS-based technologies
- 6.4: Clinical data-driven society
- 6.5: Future of decision support system
- 6.6: Example: Decision support system
- 6.7: Conclusion
- Section 2: Machine learning and deep learning for healthcare
- Chapter 7: A comprehensive review on deep learning techniques for a BCI-based communication system
- Abstract
- Acknowledgments
- 7.1: Introduction
- 7.2: Communication system for paralytic people
- 7.3: Acquisition system
- 7.4: Machine learning techniques in EEG signal processing
- 7.5: Deep learning techniques in EEG signal processing
- 7.6: Performance metrics
- 7.7: Inferences
- 7.8: Research challenges and opportunities
- 7.9: Future scope
- 7.10: Conclusion
- Chapter 8: Clinical diagnostic systems based on machine learning and deep learning
- Abstract
- 8.1: Introduction
- 8.2: Literature review and discussion
- 8.3: Applications of machine learning and deep learning in healthcare systems
- 8.4: Proposed methodology
- 8.5: Results and discussion
- 8.6: Future scope and perceptive
- 8.7: Conclusion
- Chapter 9: An improved time-frequency method for efficient diagnosis of cardiac arrhythmias
- Abstract
- 9.1: Introduction
- 9.2: Methods
- 9.3: Proposed methodology
- 9.4: Experiments and simulation performance
- 9.5: Conclusion and future scope
- Chapter 10: Local plastic surgery-based face recognition using convolutional neural networks
- Abstract
- 10.1: Introduction
- 10.2: Overview of convolutional neural network
- 10.3: Literature survey
- 10.4: Design of deep learning architecture for local plastic surgery-based face recognition
- 10.5: Experimental setup
- 10.6: Database description
- 10.7: Results
- 10.8: Conclusion and future scope
- Chapter 11: Machine learning algorithms for prediction of heart disease
- Abstract
- Acknowledgments
- 11.1: Introduction
- 11.2: Literature review
- 11.3: ML workflow
- 11.4: Experimental setup
- 11.5: Supervised ML algorithms
- 11.6: Ensemble ML models
- 11.7: Results and discussion
- 11.8: Summary
- Chapter 12: Convolutional Siamese networks for one-shot malaria parasite recognition in microscopic images
- Abstract
- 12.1: Introduction
- 12.2: Related works
- 12.3: Materials and methods
- 12.4: Proposed methodology
- 12.5: Results and discussions
- 12.6: Conclusions
- Chapter 13: Kidney disease prediction using a machine learning approach: A comparative and comprehensive analysis
- Abstract
- 13.1: Introduction
- 13.2: Machine learning importance in disease prediction
- 13.3: ML models used in the study
- 13.4: Results and discussion
- 13.5: Conclusion
- Index
- Edition: 1
- Published: June 10, 2021
- Imprint: Academic Press
- No. of pages: 372
- Language: English
- Paperback ISBN: 9780128216330
- eBook ISBN: 9780128220443
PN
Pradeep N
Dr. Pradeep N PhD is Associate Professor in Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India affiliated Visvesvaraya Technological University, Belagavi, Karnataka, India. He has 18 years of academic experience, including teaching and research experience. His research areas of interest include machine learning, pattern recognition, medical image analysis, knowledge discovery techniques, and data analytics. He has published more than 20 research articles published in refereed journals, authored six book chapters, and edited several books. He is a reviewer of various international conferences and several journals, including Multimedia Tools and Applications, Springer. His one Indian patent application is published and one Australian patent is granted. He is a professional member in ACM, ISTE and IEI. He was awarded as "Outstanding Teacher in Computer Science and Engineering", during the 3rd Global Outreach Research and Education Summit and Awards 2019, organized by Global Outreach Research and Education Association. Dr. Pradeep N is a technical committee member for Davangere Smart City, Davangere.
Affiliations and expertise
Associate professor and Postgraduate Head, Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Karnataka, IndiaSK
Sandeep Kautish
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.
Affiliations and expertise
Professor and Director, Apex Institute of Technology (AIT - CSE), Chandigarh University, Punjab, IndiaSP
Sheng-Lung Peng
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.
Affiliations and expertise
Professor, Department of Computer Science and Information Engineering, National Dong Hwa University, TaiwanRead Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics on ScienceDirect