Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics
- Author : Pradeep Nijalingappa
- Publsiher : Academic Press
- Release : 15 June 2021
- ISBN : 0128216336
- Page : 332 pages
- Rating : 4/5 from 21 voters
Download or read online book entitled Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics written by Pradeep Nijalingappa and published by Academic Press. This book was released on 15 June 2021 with total page 332 pages. Available in PDF, EPUB and Kindle. Get best books that you want by click Get Book Button and Read as many books as you like. Book Excerpt : 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