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.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Contributors - p. xiii 6. Preface - p. xvii 7. Acknowledgments - p. xix 8. Chapter 1: Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges - p. xix 9. Chapter 2: Big Data Analytics Challenges and Solutions - p. 17 10. Chapter 3: Big Data Analytics in Healthcare: A Critical Analysis - p. 41 11. Chapter 4: Transfer Learning and Supervised Classifier Based Prediction Model for Breast Cancer - p. 57 12. Chapter 5: Chronic TTH Analysis by EMG and GSR Biofeedback on Various Modes and Various Medical Symptoms Using IoT - p. 86 13. Chapter 6: Multilevel Classification Framework of fMRI Data: A Big Data Approach - p. 150 14. Chapter 7: Smart Healthcare: An Approach for Ubiquitous Healthcare Management Using IoT - p. 174 15. Chapter 8: Blockchain in Healthcare: Challenges and Solutions - p. 196 16. Chapter 9: Intelligence-Based Health Recommendation System Using Big Data Analytics - p. 226 17. Chapter 10: Computational Biology Approach in Management of Big Data of Healthcare Sector - p. 246 18. Chapter 11: Kidney-Inspired Algorithm and Fuzzy Clustering for Biomedical Data Analysis - p. 267 19. Index - p. 283
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.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Contributors - p. xiii 6. Preface - p. xvii 7. Acknowledgments - p. xix 8. Chapter 1: Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges - p. xix 9. Chapter 2: Big Data Analytics Challenges and Solutions - p. 17 10. Chapter 3: Big Data Analytics in Healthcare: A Critical Analysis - p. 41 11. Chapter 4: Transfer Learning and Supervised Classifier Based Prediction Model for Breast Cancer - p. 57 12. Chapter 5: Chronic TTH Analysis by EMG and GSR Biofeedback on Various Modes and Various Medical Symptoms Using IoT - p. 86 13. Chapter 6: Multilevel Classification Framework of fMRI Data: A Big Data Approach - p. 150 14. Chapter 7: Smart Healthcare: An Approach for Ubiquitous Healthcare Management Using IoT - p. 174 15. Chapter 8: Blockchain in Healthcare: Challenges and Solutions - p. 196 16. Chapter 9: Intelligence-Based Health Recommendation System Using Big Data Analytics - p. 226 17. Chapter 10: Computational Biology Approach in Management of Big Data of Healthcare Sector - p. 246 18. Chapter 11: Kidney-Inspired Algorithm and Fuzzy Clustering for Biomedical Data Analysis - p. 267 19. Index - p. 283
