Machine Learning, Big Data, and IoT for Medical Informatics

Front-Matter_2021_Machine-Learning--Big-Data--and-IoT-for-Medical-Informatic
	Front matter
Copyright_2021_Machine-Learning--Big-Data--and-IoT-for-Medical-Informatics
	Copyright
Contributors_2021_Machine-Learning--Big-Data--and-IoT-for-Medical-Informatic
	Contributors
Preface_2021_Machine-Learning--Big-Data--and-IoT-for-Medical-Informatics
	Preface
		Outline of the book and chapter synopses
		Special acknowledgments
Chapter-1---Predictive-analytics-and-machine-l_2021_Machine-Learning--Big-Da
	Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques
		Introduction: Predictive analytics for medical informatics
			Overview: Goals of machine learning
			Current state of practice
			Key task definitions
				Diagnosis
				Predictive analytics
				Therapy recommendation
				Automation of treatment
				Other tasks in integrative medicine
			Open research problems
				Learning for classification and regression
				Learning to act: Control and planning
				Toward greater autonomy: Active learning and self-supervision
		Background
			Diagnosis
				Diagnostic classification and regression tasks
				Diagnostic policy-learning tasks
				Active, transfer, and self-supervised learning
			Predictive analytics
				Prediction by classification and regression
				Learning to predict from reinforcements and by supervision
				Transfer learning in prediction
			Therapy recommendation
				Supervised therapy recommender systems
			Automation of treatment
				Classification and regression-based tasks
				RL for automation
				Active learning in automation
			Integrating medical informatics and health informatics
				Classification and regression tasks in HMI
				Reinforcement learning for HMI
				Self-supervised, transfer, and active learning in HMI
		Techniques for machine learning
			Supervised, unsupervised, and semisupervised learning
				Shallow
				Deep
			Reinforcement learning
				Traditional
				Deep RL
			Self-supervised, transfer, and active learning
				Traditional
				Deep
		Applications
			Test beds for diagnosis and prognosis
				New test beds
			Test beds for therapy recommendation and automation
				Prescriptions
				Surgery
		Experimental results
			Test bed
			Results and discussion
		Conclusion: Machine learning for computational medicine
			Frontiers: Preclinical, translational, and clinical
			Toward the future: Learning and medical automation
		References
Chapter-2---Geolocation-aware-IoT-and-cl_2021_Machine-Learning--Big-Data--an
	Geolocation-aware IoT and cloud-fog-based solutions for healthcare
		Introduction
		Related work
			Health monitoring system with cloud computing
			Health monitoring system with fog computing
			Health monitoring system with cloud-fog computing
		Proposed framework
			Health data analysis
			Geospatial analysis for medical facility
				Overlay analysis to obtain nearest medical facilities
				Shortest path to reach nearest medical centers
			Delay and power consumption calculation
		Performance evaluation
		Conclusion and future work
		References
Chapter-3---Machine-learning-vulnera_2021_Machine-Learning--Big-Data--and-Io
	Machine learning vulnerability in medical imaging
		Introduction
		Computer vision
		Adversarial computer vision
		Methods to produce adversarial examples
		Adversarial attacks
		Adversarial defensive methods
		Adversarial computer vision in medical imaging
		Adversarial examples: How to generate?
		Conclusion
		Acknowledgment
		References
Chapter-4---Skull-stripping-and-tumo_2021_Machine-Learning--Big-Data--and-Io
	Skull stripping and tumor detection using 3D U-Net
		Introduction
			Previous work
		Overview of U-net architecture
			3D U-net
				Batch normalization
				Activation function
				Pooling
				Padding
				Optimizer
		Materials and methods
			Dataset
			Implementation
		Results
			Experimental result
				Dice coefficient
				Accuracy
				Intersection over Union (IoU)
			Quantitative result
			Qualitative result
		Conclusion
		References
Chapter-5---Cross-color-dominant-deep-autoencoder-fo_2021_Machine-Learning--
	Cross color dominant deep autoencoder for quality enhancement of laparoscopic video: A hybrid deep learning an
		Introduction
		Range-domain filtering
		Cross color dominant deep autoencoder (C2D2A) leveraging color spareness and saliency
			Evolution of DCM through C2D2A
			Inclusion of DCM into principal flow of bilateral filtering
		Experimental results
		Conclusion
		Acknowledgments
		References
Chapter-6---Estimating-the-respiratory-rate-_2021_Machine-Learning--Big-Data
	Estimating the respiratory rate from ECG and PPG using machine learning techniques
		Introduction
			Motivation
			Background
		Related work
		Methods
			Data
			Steps
			RR signal extraction
			Machine learning
		Experimental results
		Discussion and conclusion
		Acknowledgments
		References
Chapter-7---Machine-learning-enabled-Inte_2021_Machine-Learning--Big-Data--a
	Machine learning-enabled Internet of Things for medical informatics
		Introduction
			Healthcare Internet of Things
				H-IoT architecture
				Three-tier H-IoT architecture
		Applications and challenges of H-IoT
			Applications of H-IoT
				Fitness tracking
				Neurological disorders
				Cardio vascular disorders
				Ambient-assisted living
			Challenges of H-IoT system
				QoS improvement
				Scalability challenges
		Machine learning
			Machine learning advancements at the application level of H-IoT
			Machine learning advancements at network level of H-IoT
		Future research directions
			Novel applications of ML in H-IoT
				Real-time monitoring and treatment
				Training for professionals
				Advanced prosthetics
			Research opportunities in network management
				Channel access
				Dynamic data management
				Fully autonomous operation
				Security
		Conclusion
		References
Chapter-8---Edge-detection-based-segmen_2021_Machine-Learning--Big-Data--and
	Edge detection-based segmentation for detecting skin lesions
		Introduction
		Previous works
		Materials and methods
			Elitist-Jaya algorithm
			Otsus method
		Proposed method
			Image preprocessing
			Edge detection
		Experiment and results
			Dataset
			Evaluation metrics
			Results and discussion
			Statistical analysis
		Conclusion
		References
Chapter-9---A-review-of-deep-learning-appr_2021_Machine-Learning--Big-Data--
	A review of deep learning approaches in glove-based gesture classification
		Introduction
		Data gloves
			Early and commercial data gloves
			Sensing mechanism in data gloves
				Fiber-optic sensors
				Conductive strain sensors
				Inertial sensors
		Gesture taxonomies
		Gesture classification
			Classical machine learning algorithms
				K-nearest neighbor
				Support vector machine (SVM)
				Decision tree
				Artificial neural network (ANN)
				Probabilistic neural network (PNN)
			Glove-based gesture classification with classical machine learning algorithms
			Deep learning
				Convolutional neural network (CNN)
				Recurrent neural network (RNN)
			Glove-based gesture classification using deep learning
		Discussion and future trends
		Conclusion
		References
Chapter-10---An-ensemble-approach-for-evaluati_2021_Machine-Learning--Big-Da
	An ensemble approach for evaluating the cognitive performance of human population at high altitude
		Introduction
		Methodology
			Data collection
			Data processing and feature selection
			Differential expression analyses
			Association rule mining
			Experimental set-up
		Results and discussion
			Differential analyses-Cognitive and clinical features
			Discovered associative rules
			Discussion
		Future opportunities
		Conclusions
		Acknowledgment
		References
Chapter-11---Machine-learning-in-expert-sys_2021_Machine-Learning--Big-Data-
	Machine learning in expert systems for disease diagnostics in human healthcare
		Introduction
		Types of expert systems
		Components of an expert system
		Techniques used in expert systems of medical diagnosis
		Existing expert systems
		Case studies
			Cancer diagnosis using rule-based expert system
			Alzheimers diagnosis using fuzzy-based expert systems
				Algorithm of fuzzy inference system
		Significance and novelty of expert systems
		Limitations of expert systems
		Conclusion
		Acknowledgment
		References
Chapter-12---An-entropy-based-hybrid-featu_2021_Machine-Learning--Big-Data--
	An entropy-based hybrid feature selection approach for medical datasets
		Introduction
			Deficiencies of the existing models
			Chapter organization
		Background of the present research
			Feature selection (FS)
		Methodology
			The entropy based feature selection approach
				Equi-class distribution of instances
				Splitting the dataset D into subsets: D1, D2, and D3
		Experiment and experimental results
			Experiment using suggested feature selection approach
		Discussion
			Performance analysis of the suggested feature selection approach
		Conclusions and future works
		Conflict of interest
		Appendix A
			Explanation on entropy-based feature extraction approach
		References
Chapter-13---Machine-learning-for-opt_2021_Machine-Learning--Big-Data--and-I
	Machine learning for optimizing healthcare resources
		Introduction
		The state of the art
			Resource management
			Impact on peoples health
			Exit strategies
		Machine learning for health data analysis
		Feature selection techniques
			Filter approach
				Correlation based
				Information based
				Consistency based
				Distance based
			Wrapper approach
				Classic search algorithms
					Greedy search
					Best first search
				Metaheuristic algorithms
					Genetic algorithms
					Particle swarm optimization
					Ant colony optimization
					Artificial bee colony
					Grey wolf optimization
					Artificial immune system algorithms
					Gravitational search optimization
			Embedded approach
		Machine learning classifiers
			One-class vs. multiclass classification
			Supervised vs. unsupervised learning
		Case studies
			Experimental setup
			Case study 1: Diabetes data analysis
		Case study 2: COVID-19 data analysis
		Summary and future directions
		References
Chapter-14---Interpretable-semisupervised_2021_Machine-Learning--Big-Data--a
	Interpretable semisupervised classifier for predicting cancer stages
		Introduction
		Self-labeling gray box
		Data preparation
		Experiments and discussion
			Influence of clinical and proteomic data on the prediction of cancer stage
			Influence of unlabeled data on the prediction of cancer stage
			Influence of unlabeled data on the prediction of cancer stage for rare cancer types
		Conclusions
		Acknowledgments
		References
Chapter-15---Applications-of-blockchain-t_2021_Machine-Learning--Big-Data--a
	Applications of blockchain technology in smart healthcare: An overview
		Introduction
			Comparison to other surveys
		Blockchain overview
			Key requirements
		Proposed healthcare monitoring framework
		Blockchain-enabled healthcare applications
		Potential challenges
		Concluding remarks
		Declaration of competing interest
		References
Chapter-16---Prediction-of-leukemia-by-cl_2021_Machine-Learning--Big-Data--a
	Prediction of leukemia by classification and clustering techniques
		Introduction
		Motivation
		Literature review
		Description of proposed system
			Introduction and related concepts
			Framework for the proposed system
				Support vector machine
				K-nearest neighbor
				K-means clustering
				Fuzzy c-means clustering
		Simulation results and discussion
		Conclusion and future directions
		References
Chapter-17---Performance-evaluation-of-fractal-_2021_Machine-Learning--Big-D
	Performance evaluation of fractal features toward seizure detection from electroencephalogram signals
		Introduction
		Fractal dimension
			Katz fractal dimension
			Higuchi fractal dimension
			Petrosian fractal dimension
		Dataset
		Experiments
		Results and discussion
		Conclusion
		Acknowledgments
		References
Chapter-18---Integer-period-discrete-Fourier-tran_2021_Machine-Learning--Big
	Integer period discrete Fourier transform-based algorithm for the identification of tandem repeats in the DNA ...
		Introduction
		Related work
		Algorithm for detection of TRs
			DNA sequences
			Numerical mapping
			Short time integer period discrete Fourier transform
			Thresholding
			Verification of the detected candidate TRs
		Performance analysis of the proposed algorithm
		Conclusion
		References
Chapter-19---A-blockchain-solution-for-t_2021_Machine-Learning--Big-Data--an
	A blockchain solution for the privacy of patients medical data
		Introduction
		Stakeholders of healthcare industry
			Patients
			Pharmaceutical companies
			Healthcare providers (doctors, nurses, hospitals, nursing homes, clinics, etc.)
			Government
			Insurance companies
		Data protection laws for healthcare industry
		Medical data management
		Issues and challenges of healthcare industry
		Blockchain technology
			Features of blockchain
			Types of blockchain
			Working of blockchain
		Blockchain applications in healthcare
		Blockchain-based framework for privacy protection of patients data
		Conclusion
		References
Chapter-20---A-novel-approach-for-securing_2021_Machine-Learning--Big-Data--
	A novel approach for securing e-health application in a cloud environment
		Introduction
			Contribution
		Motivation
			Related works
			Challenges
		Proposed system
		Conclusion
		References
Chapter-21---An-ensemble-classifier-approach-_2021_Machine-Learning--Big-Dat
	An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm
		Introduction
		Data analytics
		Machine learning
		Approaching ensemble learning
		Understanding bagging
		Exploring boosting
		Discovering stacking
			Machine learning applications for healthcare analytics
			Machine learning-based model for disease diagnosis
			Machine learning-based algorithms to identify breast cancer
			Convolutional neural networks to detect cancer cells in brain images
			Machine learning techniques to detect prostate cancer in Magnetic resonance imaging
			Classification of respiratory diseases using machine learning
			Parkinsons disease diagnosis with machine learning-based models
		Processing drug discovery with machine learning
			Analyzing clinical data using machine learning algorithms
			Predicting thyroid disease using ensemble learning
			Machine learning-based applications for thyroid disease classification
			Preprocessing the dataset
			AdaBoostM algorithm
		Conclusion
		References
Chapter-22---A-review-of-deep-learning_2021_Machine-Learning--Big-Data--and-
	A review of deep learning models for medical diagnosis
		Motivation
		Introduction
		MRI Segmentation
		Deep learning architectures used in diagnostic brain tumor analysis
			Convolutional neural networks or convnets
			Stacked autoencoders
			Deep belief networks
			2D U-Net
			3D U-Net
			Cascaded anisotropic network
		Deep learning tools applied to MRI images
		Proposed framework
		Conclusion and outlook
		Future directions
		References
Chapter-23---Machine-learning-in-_2021_Machine-Learning--Big-Data--and-IoT-f
	Machine learning in precision medicine
		Precision medicine
		Machine learning
		Machine learning in precision medicine
			Detection and diagnosis of a disease
			Prognosis of a disease
			Discovery of biomarkers and drug candidates
		Future opportunities
		Conclusions
		References
Index_2021_Machine-Learning--Big-Data--and-IoT-for-Medical-Informatics
	Index