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

✍ Scribed by Pradeep N PhD (editor), Sandeep Kautish (editor), Sheng-Lung Peng (editor)

• Publisher: Academic Press
• Year: 2021
• Tongue: English
• Leaves: 374
• Category: Library

✦ Synopsis

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians.

✦ Table of Contents

Front Cover
Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics
Copyright
Dedication
Contents
Contributors
Editors biography
Foreword
Preface
Overview
Section 1: Big data in healthcare analytics
Chapter 1: Foundations of healthcare informatics
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
1.7.1. Traditional systems
1.7.2. Evidence-based medicine
1.7.3. Artificial intelligence and statistical inference-based approaches
1.8. Healthcare information management
1.9. Control flow
1.10. Other perspectives
1.11. Conclusion
References
Chapter 2: Smart healthcare systems using big data
2.1. Introduction
2.1.1. Background and driving forces
2.2. Big data analytics in healthcare
2.2.1. Disease prediction
2.2.2. Electronic health records
2.2.3. Real-time monitoring
2.2.4. Medical strategic planning
2.2.5. Telemedicine
2.2.6. Drug suggestions
2.2.7. Medical imaging
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
References
Chapter 3: Big data-based frameworks for healthcare systems
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
3.4.1. Cloud computing and architecture
3.4.2. Fog computing and architecture
3.4.3. Internet of things (IoT)
3.4.4. Internet of medical things (IoMT)
3.4.5. Machine learning (ML)
3.4.6. Deep learning
3.4.7. Intelligent computational techniques and data mining
3.5. Overview of big data platform and tools for healthcare systems
3.5.1. Hadoop architecture
3.5.2. Apache hadoop
3.5.3. Apache spark
3.5.4. Apache storm
3.6. Proposed big data-based conceptual framework for healthcare systems
3.6.1. Proposed system functionalities
3.6.1.1. Data sources
3.6.1.2. Patient healthcare-related data
3.6.1.3. Cloud and fog computing components
3.6.1.4. Big data analytics methods, techniques, and platform tools
3.6.1.5. Patient healthcare monitoring and recommendation system
6.1.6. Healthcare research and knowledge infrastructure
3.7. Conclusion
References
Chapter 4: Predictive analysis and modeling in healthcare systems
4.1. Introduction
4.2. Process configuration and modeling in healthcare systems
4.3. Basic techniques of process modeling and prediction
4.3.1. Process discovery
4.3.2. Enhancement
4.4. Event log
4.4.1. Event and attributes
4.4.2. Case, trace, and event log
4.4.3. Structure of an event log
4.5. Control perspective of hospital process using various modeling notations
4.5.1. Transition systems
4.5.2. Petri net
4.5.3. Workflow nets
4.5.4. Yet another workflow language (YAWL)
4.5.5. Business process modeling notation (BPMN)
4.5.6. Event-driven process chains (EPC)
4.5.7. Causal nets
4.6. Predictive modeling control flow of a process using fuzzy miner
4.6.1. Hospital process
4.6.2. Hospital treatment process
4.7. Open research problems
4.8. Conclusion
References
Chapter 5: Challenges and opportunities of big data integration in patient-centric healthcare analytics using mobile networks
5.1. Introduction
5.2. Elderly health monitoring using big data
5.2.1. eHealth
5.2.2. General health issues in the elderly
5.3. Personalized monitoring and support platform (MONISAN)
5.3.1. Proposed development
5.4. Patient-centric healthcare provider using big data
5.4.1. Resource allocation in mobile networks using big data analytics: A survey
5.4.2. Healthcare analytics: A survey
5.5. Patient-centric optimization model
5.5.1. Structure model
5.5.2. Classification using naïve Bayesian
5.5.3. Reduction of data
5.5.4. Generalization of data
5.5.5. The naïve Bayesian formulation techniques used to calculate patient priority using MILP
5.5.6. Formulation of problem
5.6. The WSRMAX approach-based MILP formulation
5.6.1. The optimization techniques used before providing priority to patients
5.7. MILP formulation-probability fairness approach
5.7.1. The optimization techniques used before providing priority to patients
5.7.2. After patients prioritization
5.7.2.1. Receiving power calculation
5.8. Heuristic approach
5.9. Results and discussion
5.9.1. The WSRMAX approach-based MILP and heuristic formulation
5.9.1.1. The optimization techniques used before providing priority to patients
5.9.1.2. After patient prioritization
5.9.2. Probability fairness approach
5.9.2.1. The optimization techniques used before providing priority to patients
5.9.2.2. After patient prioritization
5.10. Future directions
5.10.1. Choice of decision-making platform
5.10.2. Ranking features and selecting the most optimized feature
5.10.3. Integration with 5G
5.10.4. Infrastructure sharing
5.10.5. Wireless drug injection
5.11. Conclusion
References
Chapter 6: Emergence of decision support systems in healthcare
6.1. Introduction
6.1.1. Overview
6.1.2. Need for CDSS
6.1.3. Types of CDSS
6.1.4. Effectiveness and applications of CDSS
6.2. Transformation in healthcare systems
6.2.1. Adoption of CDSS
6.2.2. Key findings
6.2.3. Enterprise-level adaptation
6.2.4. Health IT infrastructure
6.3. CDS-based technologies
6.3.1. Supervised learning techniques
6.3.1.1. Decision tree
6.3.1.2. Logistic regression
6.3.1.3. Neural networks
6.3.2. Unsupervised learning techniques
6.3.3. Disease diagnosis techniques
6.3.3.1. Domain selection
6.3.3.2. Knowledge base-construction
6.3.3.3. Algorithms and user interface
6.3.4. CDS-related issues
6.4. Clinical data-driven society
6.4.1. Information extraction
6.4.2. CDS today and tomorrow
6.5. Future of decision support system
6.6. Example: Decision support system
6.6.1. CDSS for liver disorder identification
6.7. Conclusion
References
Section 2: Machine learning and deep learning for healthcare
Chapter 7: A comprehensive review on deep learning techniques for a BCI-based communication system
7.1. Introduction
7.1.1. Brain signals
7.1.1.1. Brain computer interface
7.1.1.2. Electric potential source for BCI
7.1.1.3. Evoked potential
7.1.1.4. Event-related potential (ERP)
7.2. Communication system for paralytic people
7.2.1. Oculography-based control systems
7.2.2. Morse code-based assistive tool
7.2.3. Sensor-based systems
7.2.4. EEG-based systems
7.3. Acquisition system
7.3.1. Benchmark datasets
7.4. Machine learning techniques in EEG signal processing
7.4.1. Support vector machine
7.4.2. k-NN
7.4.3. Logistic regression
7.4.4. Naïve Bayes
7.5. Deep learning techniques in EEG signal processing
7.5.1. Deep learning models
7.5.1.1. Supervised deep learning
7.5.1.2. Convolutional neural network (CNN)
7.5.1.3. RNN
7.5.1.4. Unsupervised deep learning
7.5.1.5. Autoencoder
7.5.1.6. Semisupervised deep learning
7.5.1.7. Deep belief network
7.5.2. Deep learning in feature extraction
7.5.3. Deep learning for classification
7.6. Performance metrics
7.7. Inferences
7.8. Research challenges and opportunities
7.8.1. Using multivariate system
7.8.2. The dimensionality of the data
7.8.3. Artifacts
7.8.4. Unexplored areas
7.9. Future scope
7.10. Conclusion
Acknowledgments
References
Chapter 8: Clinical diagnostic systems based on machine learning and deep learning
8.1. Introduction
8.2. Literature review and discussion
8.2.1. Major findings in problem domain
8.3. Applications of machine learning and deep learning in healthcare systems
8.3.1. Heart disease diagnosis
8.3.2. Predicting diabetes
8.3.3. Prediction of liver disease
8.3.4. Robotic surgery
8.3.5. Cancer detection and prediction
8.3.6. Personalized treatment
8.3.7. Drug discovery
8.3.8. Smart EHR
8.4. Proposed methodology
8.4.1. Intraabdominal ultrasound image acquisition
8.4.2. Ultrasound image enhancement
8.4.3. Segmentation of the RoI image
8.4.4. Intraabdominal organ identification using deep neural network
8.4.5. Feature extraction
8.4.6. Abnormality identification and categorization
8.5. Results and discussion
8.6. Future scope and perceptive
8.7. Conclusion
References
Chapter 9: An improved time-frequency method for efficient diagnosis of cardiac arrhythmias
9.1. Introduction
9.2. Methods
9.2.1. Dual tree wavelet transform
9.2.2. Support vector machines (SVMs)
9.2.3. PSO technique
9.3. Proposed methodology
9.3.1. Database
9.3.2. Denoising
9.3.3. QRS wave localization and windowing
9.3.4. Input representation
9.3.5. Feature classification
9.3.6. Performance metrics
9.4. Experiments and simulation performance
9.4.1. Evaluation in patient-specific scheme
9.4.2. Advantages of proposed method
9.5. Conclusion and future scope
References
Chapter 10: Local plastic surgery-based face recognition using convolutional neural networks
10.1. Introduction
10.2. Overview of convolutional neural network
10.2.1. Convolutional layer
10.2.2. Pooling layers
10.2.3. Fully connected layers
10.2.4. Activation functions
10.2.5. CNN architectures
10.2.5.1. LeNet
10.2.5.2. AlexNet
10.2.5.3. ZFNet
10.2.5.4. VGG
10.2.5.5. GoogLeNet
10.2.5.6. ResNet
10.3. Literature survey
10.4. Design of deep learning architecture for local plastic surgery-based face recognition
10.4.1. Proposed CNN model
10.4.1.1. Convolution layer
10.4.1.2. Pooling layer
10.4.1.3. Fully connected layer
10.4.1.4. Parameter tuning
10.5. Experimental setup
10.6. Database description
10.7. Results
10.8. Conclusion and future scope
References
Chapter 11: Machine learning algorithms for prediction of heart disease
11.1. Introduction
11.1.1. Introduction to ML
11.1.2. Types of ML
11.1.2.1. Supervised learning
11.1.2.2. Unsupervised learning
11.1.2.3. Semisupervised learning
11.1.2.4. Reinforcement learning
11.2. Literature review
11.3. ML workflow
11.3.1. Data collection
11.3.2. Cleaning and preprocessing
11.3.3. Feature selection
11.3.4. Model selection
11.3.5. Training and evaluation
11.4. Experimental setup
11.5. Supervised ML algorithms
11.5.1. Support vector machine
11.5.2. Logistic regression
11.5.3. Decision tree
11.5.4. Naive Bayes classifier
11.6. Ensemble ML models
11.6.1. Majority voting
11.6.2. Weighted average voting
11.6.3. Bagging
11.6.4. Gradient boosting
11.7. Results and discussion
11.7.1. Visualization of performance metrics of base learners
11.7.2. Visualization of performance metrics of ensemble learners
11.8. Summary
Acknowledgments
References
Chapter 12: Convolutional Siamese networks for one-shot malaria parasite recognition in microscopic images
12.1. Introduction
12.2. Related works
12.2.1. State-of-the-art methods for one-shot learning
12.2.2. Siamese network for face recognition and verification
12.2.3. Siamese network for scene detection and object tracking
12.2.4. Siamese network for two-stage learning and recognition
12.2.5. Siamese network for medical applications
12.2.6. Siamese network for visual tracking and object tracking
12.2.7. Siamese network for natural language processing
12.3. Materials and methods
12.4. Proposed methodology
12.4.1. Siamese neural architecture
12.4.2. Training, parameter tuning, and evaluation
12.5. Results and discussions
12.6. Conclusions
References
Chapter 13: Kidney disease prediction using a machine learning approach: A comparative and comprehensive analysis
13.1. Introduction
13.1.1. Causes of chronic kidney disease
13.1.2. Detection of chronic kidney disease
13.1.3. Treatments for chronic kidney disease
13.2. Machine learning importance in disease prediction
13.3. ML models used in the study
13.3.1. KNN classifier
13.3.2. Logistic regression
13.3.3. Support vector machine
13.3.4. Random forest
13.3.5. Naïve Bayes
13.3.6. Artificial neural network
13.3.7. AdaBoost
13.4. Results and discussion
13.4.1. Quality measurement
13.4.1.1. Information gain
13.4.1.2. Gain ratio
13.4.1.3. Gini Index
13.4.1.4. Chi-squared distribution
13.4.1.5. FCBF
13.4.2. Evaluation techniques
13.4.2.1. Confusion matrix
13.4.2.2. Receiver operating characteristic (ROC) curve
13.4.3. Dataset description
13.4.4. Model configurations
13.4.5. Result analysis with information gain
13.4.6. Result analysis with information gain ratio
13.4.7. Result analysis with Gini Index
13.4.8. Result analysis with chi-square
13.5. Conclusion
References
Index
Back Cover