Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

Cover image
Title page
Table of Contents
Copyright
Dedication
Contributors
Editors biography
Foreword
Preface
Overview
Section 1: Big data in healthcare analytics
    Chapter 1: Foundations of healthcare informatics
        Abstract
        1.1: Introduction
        1.2: Goals of healthcare informatics
        1.3: Focus of healthcare informatics
        1.4: Applications of healthcare informatics
        1.5: Medical information
        1.6: Clinical decision support systems
        1.7: Developing clinical decision support systems
        1.8: Healthcare information management
        1.9: Control flow
        1.10: Other perspectives
        1.11: Conclusion
    Chapter 2: Smart healthcare systems using big data
        Abstract
        2.1: Introduction
        2.2: Big data analytics in healthcare
        2.3: Related work
        2.4: Big data for biomedicine
        2.5: Proposed solutions for smart healthcare model
        2.6: Role of sensor technology for eHealth
        2.7: Major applications and challenges
        2.8: Conclusion and future scope
    Chapter 3: Big data-based frameworks for healthcare systems
        Abstract
        3.1: Introduction
        3.2: The role of big data in healthcare systems and industry
        3.3: Big data frameworks for healthcare systems
        3.4: Overview of big data techniques and technologies supporting healthcare systems
        3.5: Overview of big data platform and tools for healthcare systems
        3.6: Proposed big data-based conceptual framework for healthcare systems
        3.7: Conclusion
    Chapter 4: Predictive analysis and modeling in healthcare systems
        Abstract
        4.1: Introduction
        4.2: Process configuration and modeling in healthcare systems
        4.3: Basic techniques of process modeling and prediction
        4.4: Event log
        4.5: Control perspective of hospital process using various modeling notations
        4.6: Predictive modeling control flow of a process using fuzzy miner
        4.7: Open research problems
        4.8: Conclusion
    Chapter 5: Challenges and opportunities of big data integration in patient-centric healthcare analytics using mobile networks
        Abstract
        5.1: Introduction
        5.2: Elderly health monitoring using big data
        5.3: Personalized monitoring and support platform (MONISAN)
        5.4: Patient-centric healthcare provider using big data
        5.5: Patient-centric optimization model
        5.6: The WSRMAX approach-based MILP formulation
        5.7: MILP formulation-probability fairness approach
        5.8: Heuristic approach
        5.9: Results and discussion
        5.10: Future directions
        5.11: Conclusion
    Chapter 6: Emergence of decision support systems in healthcare
        Abstract
        6.1: Introduction
        6.2: Transformation in healthcare systems
        6.3: CDS-based technologies
        6.4: Clinical data-driven society
        6.5: Future of decision support system
        6.6: Example: Decision support system
        6.7: Conclusion
Section 2: Machine learning and deep learning for healthcare
    Chapter 7: A comprehensive review on deep learning techniques for a BCI-based communication system
        Abstract
        Acknowledgments
        7.1: Introduction
        7.2: Communication system for paralytic people
        7.3: Acquisition system
        7.4: Machine learning techniques in EEG signal processing
        7.5: Deep learning techniques in EEG signal processing
        7.6: Performance metrics
        7.7: Inferences
        7.8: Research challenges and opportunities
        7.9: Future scope
        7.10: Conclusion
    Chapter 8: Clinical diagnostic systems based on machine learning and deep learning
        Abstract
        8.1: Introduction
        8.2: Literature review and discussion
        8.3: Applications of machine learning and deep learning in healthcare systems
        8.4: Proposed methodology
        8.5: Results and discussion
        8.6: Future scope and perceptive
        8.7: Conclusion
    Chapter 9: An improved time-frequency method for efficient diagnosis of cardiac arrhythmias
        Abstract
        9.1: Introduction
        9.2: Methods
        9.3: Proposed methodology
        9.4: Experiments and simulation performance
        9.5: Conclusion and future scope
    Chapter 10: Local plastic surgery-based face recognition using convolutional neural networks
        Abstract
        10.1: Introduction
        10.2: Overview of convolutional neural network
        10.3: Literature survey
        10.4: Design of deep learning architecture for local plastic surgery-based face recognition
        10.5: Experimental setup
        10.6: Database description
        10.7: Results
        10.8: Conclusion and future scope
    Chapter 11: Machine learning algorithms for prediction of heart disease
        Abstract
        Acknowledgments
        11.1: Introduction
        11.2: Literature review
        11.3: ML workflow
        11.4: Experimental setup
        11.5: Supervised ML algorithms
        11.6: Ensemble ML models
        11.7: Results and discussion
        11.8: Summary
    Chapter 12: Convolutional Siamese networks for one-shot malaria parasite recognition in microscopic images
        Abstract
        12.1: Introduction
        12.2: Related works
        12.3: Materials and methods
        12.4: Proposed methodology
        12.5: Results and discussions
        12.6: Conclusions
    Chapter 13: Kidney disease prediction using a machine learning approach: A comparative and comprehensive analysis
        Abstract
        13.1: Introduction
        13.2: Machine learning importance in disease prediction
        13.3: ML models used in the study
        13.4: Results and discussion
        13.5: Conclusion
Index