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Big data analytics for intelligent healthcare management / volume editors, Nilanjan Dey, Himansu Das, Bighnaraj Naik, Himansu Sekhar Behera.

Contributor(s): Dey, Nilanjan, 1984- [editor.] | Das, Himansu [editor.] | Naik, Bighnaraj [editor.] | Behera, Himansu Sekhar [editor.].
Material type: materialTypeLabelBookSeries: Advances in ubiquitous sensing applications for healthcare: volume three.Publisher: London : Academic Press, an imprint of Elsevier, [2019]Copyright date: 2019Description: 1 online resource (xix, 292 pages) : illustrations.Content type: text Media type: computer Carrier type: online resourceISBN: 9780128181478; 0128181478; 9780128181461; 012818146X.Subject(s): Computer science | Computer system failures | Database management | Data mining | Image processing | Big Data | Data Mining -- methods | Data Science -- methods | Computing Methodologies | Biomedical Research -- methods | COMPUTERS -- Databases -- General | Computer science | Computer system failures | Data mining | Database management | Image processingGenre/Form: Electronic books.DDC classification: 005.74 Online resources: Click Here-ScienceDirect eBooks (In-Campus Access) | Click Here-ScienceDirect eBooks (Off-Campus Access)
Contents:
Bio-inspired algorithms for big data analytics: a survey, taxonomy, and open challenges -- Big data analytics challenges and solutions -- Big data analytics in healthcare: a critical analysis -- Transfer learning and supervised classifier based prediction model for breast cancer -- Chronic TTH analysis by EMG and GSR biofeedback on various modes and various medical symptoms using IoT -- Multilevel classification framework of fMRI data: a big data approach -- Smart healthcare: an approach for ubiquitous healthcare management using IoT -- Blockchain in healthcare: challenges and solutions -- Intelligence-based health recommendation system using big data analytics -- Computational biology approach in management of big data of healthcare sector -- Kidney-inspired algorithm and fuzzy clustering for biomedical data analysis.
Summary: Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data.
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Includes bibliographical references and index.

Bio-inspired algorithms for big data analytics: a survey, taxonomy, and open challenges -- Big data analytics challenges and solutions -- Big data analytics in healthcare: a critical analysis -- Transfer learning and supervised classifier based prediction model for breast cancer -- Chronic TTH analysis by EMG and GSR biofeedback on various modes and various medical symptoms using IoT -- Multilevel classification framework of fMRI data: a big data approach -- Smart healthcare: an approach for ubiquitous healthcare management using IoT -- Blockchain in healthcare: challenges and solutions -- Intelligence-based health recommendation system using big data analytics -- Computational biology approach in management of big data of healthcare sector -- Kidney-inspired algorithm and fuzzy clustering for biomedical data analysis.

Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data.

Online resource; title from electronic title page (ProQuest Ebook Central, viewed October 25, 2019).

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