Machine Learning, Big Data, and IoT for Medical Informatics

✍ Scribed by Pardeep Kumar, Yugal Kumar, Mohamed A. Tawhid

• Publisher: Academic Press
• Year: 2021
• Tongue: English
• Leaves: 433
• Series: Intelligent Data-Centric Systems: Sensor Collected Intelligence
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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