Machine Learning, Big Data, and IoT for Medical Informatics

Machine Learning, Big Data, and IoT for Medical Informatics

by Pardeep Kumar, Yugal Kumar, Mohamed A. Tawhid, Fatos Xhafa
Released June 2021
Publisher(s): Academic Press
ISBN: 9780128217818

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Book description

Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics.

In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data.

This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.

  • Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems.
  • Includes several privacy preservation techniques for medical data.
  • Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis.
  • Offers case studies and applications relating to machine learning, big data, and health care analysis.

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Contributors
  6. Preface
    1. Outline of the book and chapter synopses
    2. Special acknowledgments
  7. Chapter 1: Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques
    1. Abstract
    2. 1: Introduction: Predictive analytics for medical informatics
    3. 2: Background
    4. 3: Techniques for machine learning
    5. 4: Applications
    6. 5: Experimental results
    7. 6: Conclusion: Machine learning for computational medicine
  8. Chapter 2: Geolocation-aware IoT and cloud-fog-based solutions for healthcare
    1. Abstract
    2. 1: Introduction
    3. 2: Related work
    4. 3: Proposed framework
    5. 4: Performance evaluation
    6. 5: Conclusion and future work
  9. Chapter 3: Machine learning vulnerability in medical imaging
    1. Abstract
    2. Acknowledgment
    3. 1: Introduction
    4. 2: Computer vision
    5. 3: Adversarial computer vision
    6. 4: Methods to produce adversarial examples
    7. 5: Adversarial attacks
    8. 6: Adversarial defensive methods
    9. 7: Adversarial computer vision in medical imaging
    10. 8: Adversarial examples: How to generate?
    11. 9: Conclusion
  10. Chapter 4: Skull stripping and tumor detection using 3D U-Net
    1. Abstract
    2. 1: Introduction
    3. 2: Overview of U-net architecture
    4. 3: Materials and methods
    5. 4: Results
    6. 5: Conclusion
  11. Chapter 5: Cross color dominant deep autoencoder for quality enhancement of laparoscopic video: A hybrid deep learning and range-domain filtering-based approach
    1. Abstract
    2. Acknowledgments
    3. 1: Introduction
    4. 2: Range-domain filtering
    5. 3: Cross color dominant deep autoencoder (C2D2A) leveraging color spareness and saliency
    6. 4: Experimental results
    7. 5: Conclusion
  12. Chapter 6: Estimating the respiratory rate from ECG and PPG using machine learning techniques
    1. Abstract
    2. Acknowledgments
    3. 1: Introduction
    4. 2: Related work
    5. 3: Methods
    6. 4: Experimental results
    7. 5: Discussion and conclusion
  13. Chapter 7: Machine learning-enabled Internet of Things for medical informatics
    1. Abstract
    2. 1: Introduction
    3. 2: Applications and challenges of H-IoT
    4. 3: Machine learning
    5. 4: Future research directions
    6. 5: Conclusion
  14. Chapter 8: Edge detection-based segmentation for detecting skin lesions
    1. Abstract
    2. 1: Introduction
    3. 2: Previous works
    4. 3: Materials and methods
    5. 4: Proposed method
    6. 5: Experiment and results
    7. 6: Conclusion
  15. Chapter 9: A review of deep learning approaches in glove-based gesture classification
    1. Abstract
    2. 1: Introduction
    3. 2: Data gloves
    4. 3: Gesture taxonomies
    5. 4: Gesture classification
    6. 5: Discussion and future trends
    7. 6: Conclusion
  16. Chapter 10: An ensemble approach for evaluating the cognitive performance of human population at high altitude
    1. Abstract
    2. Acknowledgment
    3. 1: Introduction
    4. 2: Methodology
    5. 3: Results and discussion
    6. 4: Future opportunities
    7. 5: Conclusions
  17. Chapter 11: Machine learning in expert systems for disease diagnostics in human healthcare
    1. Abstract
    2. Acknowledgment
    3. 1: Introduction
    4. 2: Types of expert systems
    5. 3: Components of an expert system
    6. 4: Techniques used in expert systems of medical diagnosis
    7. 5: Existing expert systems
    8. 6: Case studies
    9. 7: Significance and novelty of expert systems
    10. 8: Limitations of expert systems
    11. 9: Conclusion
  18. Chapter 12: An entropy-based hybrid feature selection approach for medical datasets
    1. Abstract
    2. 1: Introduction
    3. 2: Background of the present research
    4. 3: Methodology
    5. 4: Experiment and experimental results
    6. 5: Discussion
    7. 6: Conclusions and future works
    8. Conflict of interest
    9. Appendix A
  19. Chapter 13: Machine learning for optimizing healthcare resources
    1. Abstract
    2. 1: Introduction
    3. 2: The state of the art
    4. 3: Machine learning for health data analysis
    5. 4: Feature selection techniques
    6. 5: Machine learning classifiers
    7. 6: Case studies
    8. 7: Case study 2: COVID-19 data analysis
    9. 8: Summary and future directions
  20. Chapter 14: Interpretable semisupervised classifier for predicting cancer stages
    1. Abstract
    2. Acknowledgments
    3. 1: Introduction
    4. 2: Self-labeling gray box
    5. 3: Data preparation
    6. 4: Experiments and discussion
    7. 5: Conclusions
  21. Chapter 15: Applications of blockchain technology in smart healthcare: An overview
    1. Abstract
    2. 1: Introduction
    3. 2: Blockchain overview
    4. 3: Proposed healthcare monitoring framework
    5. 4: Blockchain-enabled healthcare applications
    6. 5: Potential challenges
    7. 6: Concluding remarks
  22. Chapter 16: Prediction of leukemia by classification and clustering techniques
    1. Abstract
    2. 1: Introduction
    3. 2: Motivation
    4. 3: Literature review
    5. 4: Description of proposed system
    6. 5: Simulation results and discussion
    7. 6: Conclusion and future directions
  23. Chapter 17: Performance evaluation of fractal features toward seizure detection from electroencephalogram signals
    1. Abstract
    2. Acknowledgments
    3. 1: Introduction
    4. 2: Fractal dimension
    5. 3: Dataset
    6. 4: Experiments
    7. 5: Results and discussion
    8. 6: Conclusion
  24. Chapter 18: Integer period discrete Fourier transform-based algorithm for the identification of tandem repeats in the DNA sequences
    1. Abstract
    2. 1: Introduction
    3. 2: Related work
    4. 3: Algorithm for detection of TRs
    5. 4: Performance analysis of the proposed algorithm
    6. 5: Conclusion
  25. Chapter 19: A blockchain solution for the privacy of patients’ medical data
    1. Abstract
    2. 1: Introduction
    3. 2: Stakeholders of healthcare industry
    4. 3: Data protection laws for healthcare industry
    5. 4: Medical data management
    6. 5: Issues and challenges of healthcare industry
    7. 6: Blockchain technology
    8. 7: Blockchain applications in healthcare
    9. 8: Blockchain-based framework for privacy protection of patient’s data
    10. 9: Conclusion
  26. Chapter 20: A novel approach for securing e-health application in a cloud environment
    1. Abstract
    2. 1: Introduction
    3. 2: Motivation
    4. 3: Proposed system
    5. 4: Conclusion
  27. Chapter 21: An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm
    1. Abstract
    2. 1: Introduction
    3. 2: Data analytics
    4. 3: Machine learning
    5. 4: Approaching ensemble learning
    6. 5: Understanding bagging
    7. 6: Exploring boosting
    8. 7: Discovering stacking
    9. 8: Processing drug discovery with machine learning
    10. 9: Conclusion
  28. Chapter 22: A review of deep learning models for medical diagnosis
    1. Abstract
    2. 1: Motivation
    3. 2: Introduction
    4. 3: MRI Segmentation
    5. 4: Deep learning architectures used in diagnostic brain tumor analysis
    6. 5: Deep learning tools applied to MRI images
    7. 6: Proposed framework
    8. 7: Conclusion and outlook
    9. 8: Future directions
  29. Chapter 23: Machine learning in precision medicine
    1. Abstract
    2. 1: Precision medicine
    3. 2: Machine learning
    4. 3: Machine learning in precision medicine
    5. 4: Future opportunities
    6. 5: Conclusions
  30. Index

Product information

  • Title: Machine Learning, Big Data, and IoT for Medical Informatics
  • Author(s): Pardeep Kumar, Yugal Kumar, Mohamed A. Tawhid, Fatos Xhafa
  • Release date: June 2021
  • Publisher(s): Academic Press
  • ISBN: 9780128217818