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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

  • ID: 5230526
  • Book
  • June 2021
  • 332 Pages
  • Elsevier Science and Technology

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents how these emerging areas are changing the world of data utilization, especially in clinical healthcare. Various techniques, methodologies and algorithms are presented in a structured manner to assist physicians in the precision care of patients and help biomedical engineers and computers scientists understand the impact of these techniques on healthcare analytics. Sections cover Big Data aspects, i.e., healthcare Decision Support Systems and Analytics related topics, focus on current frameworks and applications of Deep Learning and Machine Learning, and provide an outlook on future directions.

The entire book takes a case study approach, providing a wealth of real-world case studies that 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
  • Presents a unique case study approach that provides readers with insights for practical clinical implementations
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Part I: Big Data in Healthcare Analytics 1. Foundations of Healthcare Informatics 2. Smart Healthcare Systems Using Big Data 3. Big Data-based Frameworks for Healthcare Systems 4. Predictive Analysis and Modelling in Healthcare Systems 5. Challenges and Opportunities of Big Data Integration in Patient-Centric Healthcare Analytics Using Mobile Networks 6. Emergence of Decision Support Systems in Healthcare

Part II: Machine Learning and Deep Learning for Healthcare 7. A Comprehensive Review on Deep Learning Techniques for BCI-based Communication Systems 8. Machine Learning and Deep Learning-based Clinical Diagnostic Systems 9. An Improved Time-Frequency Method for Efficient Diagnosis of Cardiac Arrhythmias 10. Local Plastic Surgery-based Face Recognition Using Convolutional Neural Networks 11. Machine Learning Algorithms for Prediction of Heart Disease 12. Convolutional Siamese Networks for One-Shot Malaria Parasites Recognition in Microscopic Images 13. Kidney Disease Prediction Using a Machine Learning Approach: A Comparative and Comprehensive Analysis

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Nijalingappa, Pradeep
Dr Pradeep N is an academician with 16 years of teaching experience which includes 8 years of research experience. He has worked at various levels: Lecturer, Sr. Lecturer and Asst. Professor in an esteemed Engineering institution. He completed his Masters' degree in Computer Engineering in 2002 and doctorate degree in Computer Science and Engineering in 2014. He has published his research articles in various Journals and Conferences and having good citations too. He has guided for more than 30 UG students' project batches and 20 students in PG projects. Presently working as Associate Professor and PG Head in Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India and supervising 3 students at Ph.D. level. He is reachable
Mob. No.: +91-9886086840 and Email ID: nmnpradeep@gmail.com
Kautish, Sandeep
Dr. Sandeep Kautish is a seasoned academician with his doctorate degree in Computer Science specializing in
Social Network Analytics. Presently he is working as a Professor and Dean of Academics at the LBEF Campus,
Asia Pacific University, Kathmandu, Nepal.
Peng, Sheng Lung
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.
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