Big Data Analytics for Intelligent Healthcare Management

Book description

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.

  • Examines the methodology and requirements for development of big data architecture, big data modeling, big data as a service, big data analytics, and more
  • Discusses big data applications for intelligent healthcare management, such as revenue management and pricing, predictive analytics/forecasting, big data integration for medical data, algorithms and techniques, etc.
  • Covers the development of big data tools, such as data, web and text mining, data mining, optimization, machine learning, cloud in big data with Hadoop, big data in IoT, and more

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Contributors
  6. Preface
  7. Acknowledgments
  8. Chapter 1: Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges
    1. Abstract
    2. Acknowledgments
    3. 1.1 Introduction
    4. 1.2 Big Data Analytical Model
    5. 1.3 Bio-Inspired Algorithms for Big Data Analytics: A Taxonomy
    6. 1.4 Future Research Directions and Open Challenges
    7. 1.5 Emerging Research Areas in Bio-Inspired Algorithm-Based Big Data Analytics
    8. 1.6 Summary and Conclusions
    9. Glossary
  9. Chapter 2: Big Data Analytics Challenges and Solutions
    1. Abstract
    2. 2.1 Introduction
    3. 2.2 Records Analysis Challenges
    4. 2.3 Arrangements of Challenges
    5. 2.4 Demanding Situations in Managing Huge Records
    6. 2.5 Massive Facts Equal Large Possibilities
    7. 2.6 Discussion
    8. 2.7 Conclusion
    9. Glossary
  10. Chapter 3: Big Data Analytics in Healthcare: A Critical Analysis
    1. Abstract
    2. 3.1 Introduction
    3. 3.2 Big Data
    4. 3.3 Healthcare Data
    5. 3.4 Medical Image Processing and its Role in Healthcare Data Analysis
    6. 3.5 Recent Works in Big Data Analytics in Healthcare Data
    7. 3.6 Architectural Framework and Different Tools for Big Data Analytics in Healthcare Big Data
    8. 3.7 Challenges Faced During Big Data Analytics in Healthcare
    9. 3.8 Conclusion and Future Research
  11. Chapter 4: Transfer Learning and Supervised Classifier Based Prediction Model for Breast Cancer
    1. Abstract
    2. 4.1 Introduction
    3. 4.2 Related Work
    4. 4.3 Dataset and Methodologies
    5. 4.4 Proposed Model
    6. 4.5 Implementation
    7. 4.6 Result and Analysis
    8. 4.7 Discussion
    9. 4.8 Conclusion
  12. Chapter 5: Chronic TTH Analysis by EMG and GSR Biofeedback on Various Modes and Various Medical Symptoms Using IoT
    1. Abstract
    2. Acknowledgment
    3. 5.1 Introduction and Background
    4. 5.2 Previous Studies (Literature Review)
    5. 5.3 Independent Variable: Emotional Need Fulfillment
    6. 5.4 Meditation—Effective Spiritual Tool With Approach of Biofeedback EEG
    7. 5.5 Sensor Modalities and Our Approach
    8. 5.6 Experiments and Results—Study Plot
    9. 5.7 Data Collection Procedure—Guided Meditation as per Fig. 5.7G
    10. 5.8 Results, Interpretation and Discussion
    11. 5.9 Findings in This Chapter
    12. 5.10 Future Scope, Limitations, and Possible Applications
    13. 5.11 Conclusion
  13. Chapter 6: Multilevel Classification Framework of fMRI Data: A Big Data Approach
    1. Abstract
    2. 6.1 Introduction
    3. 6.2 Related Work
    4. 6.3 Our Approach
    5. 6.4 Result Analysis
    6. 6.5 Conclusion and Future Work
  14. Chapter 7: Smart Healthcare: An Approach for Ubiquitous Healthcare Management Using IoT
    1. Abstract
    2. 7.1 Introduction
    3. 7.2 Literature Survey
    4. 7.3 Proposed Model
    5. 7.4 Implementation of the Proposed System
    6. 7.5 Simulation and Result Discussion
    7. 7.6 Conclusion
  15. Chapter 8: Blockchain in Healthcare: Challenges and Solutions
    1. Abstract
    2. 8.1 Introduction
    3. 8.2 Healthcare Big Data and Blockchain Overview
    4. 8.3 Privacy of Healthcare Big Data
    5. 8.4 How Blockchain Is Applicable for Healthcare Big Data
    6. 8.5 Blockchain Challenges and Solutions for Healthcare Big Data
    7. 8.6 Conclusion and Discussion
  16. Chapter 9: Intelligence-Based Health Recommendation System Using Big Data Analytics
    1. Abstract
    2. 9.1 Introduction
    3. 9.2 Background
    4. 9.3 Health Recommendation System
    5. 9.4 Proposed Intelligent-Based HRS
    6. 9.5 Advantages and Disadvantages of the Proposed Health Recommendation System Using Big Data Analytics
    7. 9.6 Conclusion and Future Work
  17. Chapter 10: Computational Biology Approach in Management of Big Data of Healthcare Sector
    1. Abstract
    2. Acknowledgments
    3. 10.1 Introduction
    4. 10.2 Application of Big Data Analysis
    5. 10.3 Database Management System and Next Generation Sequencing (NGS)
    6. 10.4 De novo Assembly, Re-Sequencing, Transcriptomics Sequencing and Epigenetics
    7. 10.5 Data Collection, Extraction of Genes, and Screening of Drugs
    8. 10.6 Different Algorithms Related to Docking
    9. 10.7 Molecular Interactions, Scoring Functions, and Discussion of Some Docking Examples
    10. 10.8 Conclusions
  18. Chapter 11: Kidney-Inspired Algorithm and Fuzzy Clustering for Biomedical Data Analysis
    1. Abstract
    2. Acknowledgment
    3. 11.1 Introduction
    4. 11.2 Biological Structure of the Kidney
    5. 11.3 Kidney-Inspired Algorithm
    6. 11.4 Literature Survey
    7. 11.5 Proposed Model
    8. 11.6 Results Analysis
    9. 11.7 Conclusion
  19. Index

Product information

  • Title: Big Data Analytics for Intelligent Healthcare Management
  • Author(s): Nilanjan Dey, Himansu Das, Bighnaraj Naik, H Behera
  • Release date: April 2019
  • Publisher(s): Academic Press
  • ISBN: 9780128181478