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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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Immediately download your ebook while waiting for your print delivery. No promo code is needed.
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 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.
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
1.Big Data classification: techniques and tools
2.Big Data Analytics for healthcare: theory and applications
3.Application of tools and techniques of Big data analytics for healthcare system
4.Healthcare and medical Big Data analytics
5.Big Data analytics in medical imaging
6.Big Data analytics and artificial intelligence in mental healthcare
7.Big Data based breast cancer prediction using kernel support vector machine with the Gray Wolf Optimization algorithm
8.Big Data based medical data classification using oppositional Gray Wolf Optimization with kernel ridge regression
9.An analytical hierarchical process evaluation on parameters Apps-based Data Analytics for healthcare services
10.Firefly—Binary Cuckoo Search Technique based heart disease prediction in Big Data Analytics
11.Hybrid technique for heart diseases diagnosis based on convolution neural network and long short-term memory
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Nilanjan Dey is an Associate Professor in the Department of Computer Science and Engineering, Techno International New Town, Kolkata, India. He is a visiting fellow of the University of Reading, UK. He also holds a position of Adjunct Professor at Ton Duc Thang University, Ho Chi Minh City, Vietnam. Previously, he held an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012–2015). He was awarded his PhD from Jadavpur University in 2015. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence , IGI Global, USA. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing (SpringerNature), Data-Intensive Research(SpringerNature), Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier). He was an associate editor of IET Image Processing and editorial board member of Complex & Intelligent Systems, Springer Nature. He is an editorial board member of Applied Soft Computing, Elsevier. He is having 35 authored books and over 300 publications in the area of medical imaging, machine learning, computer aided diagnosis, data mining, etc. He is the Fellow of IETE and Senior member of IEEE.