Machine Learning in Healthcare: Fundamentals and Recent Applications

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Figures
Tables
Preface
Acknowledgments
Author Bio
1 Biostatistics
	Learning Objectives
	1.1 Data and Variables
	1.2 Types of Research Studies
	1.3 Sources of Medical Data
	1.4 Measures of Central Tendency
	1.5 Data Sampling and Its Types
		1.5.1 Probability Sampling Methods
		1.5.2 Non-Probability Sampling Methods
	1.6 Statistical Significance Analysis
	1.7 Skewness
	1.8 Kurtosis
		1.8.1 Mesokurtic
		1.8.2 Leptokurtic
		1.8.3 Platykurtic
	1.9 Curve Fitting
		1.9.1 Linear and Non-Linear Relationship
		1.9.2 Use of Curve-Fitting Method
	1.10 Correlation
		1.10.1 Pearson Correlation (PC)
		1.10.2 Spearman Rank Correlation (SRC)
	1.11 Regression
		1.11.1 Linear Regression
		1.11.2 Estimation of Regression Coefficients
	1.12 Learning Outcomes
2 Probability Theory
	Learning Objectives
	2.1 Basic Concept of Probability
	2.2 Random Experiment
	2.3 Conditional Probability
		2.3.1 Types of Events
	2.4 Bayes Theorem
	2.5 Random Variable
	2.6 Distribution Functions
		2.6.1 Binomial Distribution
		2.6.2 Poisson Distribution
		2.6.3 Normal Distribution
	2.7 Estimation
	2.8 Standard Error
	2.9 Probability of Error
	Learning Outcomes
3 Medical Data Acquisition and Pre-Processing
	Learning Objectives
	3.1 Medical Data Formats
		3.1.1 Data Formats for Medical Images
			3.1.1.1 DICOM (Digital Imaging and Communications in Medicine)
			3.1.1.2 Analyse
			3.1.1.3 NIfTI (Neuroimaging Informatics Technology Initiative)
			3.1.1.4 MINC (Medical Imaging NetCDF)
		3.1.2 Medical Data Formats for Signals
			3.1.2.1 EDF (European Data Format)
			3.1.2.2 BDF (BioSemi Data Format)
			3.1.2.3 GDF (General Data Format)
	3.2 Data Augmentation and Generation
	3.3 Data Labelling
	3.4 Data Cleaning
		3.4.1 Statistical Approach
			3.4.1.1 Listwise Deletion
			3.4.1.2 Pairwise Deletion
			3.4.1.3 Multiple Imputation
			3.4.1.4 Maximum Likelihood Imputation
		3.4.2 Machine Learning for Data Imputation
			3.4.2.1 K-Nearest Neighbour (KNN)
			3.4.2.2 Bayesian Network (BN)
	3.5 Data Normalization
	Learning Outcomes
	References
4 Medical Image Processing
	Learning Objectives
	4.1 Medical Image Modalities, Their Applications, Advantages and Limitations
		4.1.1 Radiography
		4.1.2 Nuclear Medicine
			4.1.2.1 Positron Emission Tomography (PET)
		4.1.3 Elastography
		4.1.4 Photoacoustic Imaging
		4.1.5 Tomography
			X-ray Computed Tomography (CT)
		4.1.6 Magnetic Resonance Imaging (MRI)
		4.1.7 Ultrasound Imaging Techniques
	4.2 Medical Image Enhancement
	4.3 Basics of Histogram
	4.4 Medical Image De-Noising
		4.4.1 Spatial Filtering
			4.4.1.1 Linear Filters
			4.4.1.2 Non-Linear Filters
		4.4.2 Transform Domain Filtering
			4.4.2.1 Non-Data Adaptive Transform
			4.4.2.2 Data-Adaptive Transforms
	4.5 Segmentation
	4.6 Region-Based Methods
		4.6.1 Region-Growing Segmentation
	Following Are Advantages and Disadvantages of the Region-Growing Method of Segmentation
		Advantages
		Disadvantages
	Learning Outcomes
	References
5 Bio-Signals
	Learning Objectives
	5.1 Origin of Bio-Signals
	5.2 Different Types of Bio-Signals
		5.2.1 Electrocardiogram
		5.2.2 Electroencephalogram (EEG)
		5.2.3 Electroocculogram (EOG)
		5.2.4 Electromyogram (EMG)
	5.3 Noise and Artefacts
	5.4 Filtering of Bio-Signals
	5.5 Applications of Bio-Signals
	Learning Outcome
	References
6 Feature Extraction
	Learning Objectives
	6.1 Feature Extraction
	6.2 Echographic Characteristics of Breast Tumours in Ultrasound Imaging
	6.3 Texture Feature Extraction
		6.3.1 First-Order Statistical Features
		6.3.2 Grey-Level Co-Occurrence Matrices
		6.3.3 Grey-Level Difference Statistics
		6.3.4 Neighbourhood Grey-Tone Difference Matrix
		6.3.5 Statistical Feature Matrix
		6.3.6 Texture Energy Measures
		6.3.7 Fractal Dimension Texture Analysis
		6.3.8 Spectral Measures of Texture
		6.3.9 Run-Length Texture Features
	6.4 Shape Feature Extraction
		6.4.1 Region Properties
		6.4.2 Moment Invariants
	6.5 Feature Normalization
		6.5.1 Brief Overview of Feature Normalization Techniques
	Learning Outcome
	References
7 Introduction to Machine Learning
	Learning Objectives
	7.1 Introduction: What Is Machine Learning?
	Some Terminology of Machine Learning
	7.2 Classification of Machine Learning (ML) Methods
	7.3 Steps in Implementation of Machine Learning
	Step 1: Gathering Data
	Step 2: Preparing the Data
	Step 3: Choosing a Model
	Step 4: Training
	Step 5: Evaluation
	Step 6: Hyperparameter Tuning
	Step 7: Prediction
	7.4 Training, Testing and Validation
	7.5 Machine Learning Methods
		7.5.1 Supervised Learning
	Naive Bayes Classifier
	Support Vector Machine (SVM)
	K-Nearest Neighbour Classifier
	Decision Tree Classifier
	Random Forest
	Artificial Neural Network (ANN)
		7.5.2 Unsupervised Learning
	K-Means Clustering
	Fuzzy C-Means Clustering
	7.6 Performance Evaluation of Machine Learning Model
	Learning Outcomes
	Exercise
	References
8 Cancer Detection: Breast Cancer Detection Using Mammography, Ultrasound and Magnetic Resonance Imaging (MRI)
	Learning Objectives
	8.1 Introduction
	8.2 Different Imaging Modalities
		8.2.1 Mammography (MG)
		8.2.2 Ultrasound (US)
		8.2.3 Magnetic Resonance Imaging (MRI)
	8.3 Breast Imaging Reporting and Data System (BI-RADS)
	8.4 Usefulness of Machine Learning (ML)
		8.4.1 Image Pre-Processing
		8.4.2 Image Segmentation
		8.4.3 Feature Extraction
		8.4.4 Feature Selection
		8.4.5 Classification
		8.4.6 Performance Evaluation
	8.5 Issues and Challenges
	8.6 Conclusion
	Learning Outcomes
	Exercise for Practice and Discussion
	References
9 Sickle Cell Disease Management: A Machine Learning Approach
	Learning Objectives
	9.1 Introduction
	9.2 Severity Detection of Sickle Cell Disease
		9.2.1 Analysis of Clinical Complications
		9.2.2 Analysis of Clinical Attributes
		9.2.3 Analysis of Microscopic Images of RBC
	9.3 Hydroxyurea Dosage Prediction for SCD Patients
	9.4 Patient Response to Medications Through Hydroxyurea (HU)
	9.5 SCD Management Proposed Model
	9.6 Conclusions
	Learning Outcomes
	References
10 Detection of Pulmonary Disease
	Learning Objectives
	10.1 Introduction to Pulmonary Disorders
	10.2 Restrictive and Obstructive Lung Diseases
		10.2.1 Obstructive Lung Disease
		10.2.2 Restrictive Lung Disease
	10.3 Diagnosis of Disease and Disorder
	10.4 Chest X-Ray
	10.5 CT Scan
	10.6 SPO2 Level
	10.7 Arterial Blood Gas Analysis
	10.8 Laboratory Tests
	10.9 Bronchoscopy
	10.10 Sputum Test
	10.11 Pulmonary Function Test
	10.12 Challenges and Issues
	10.13 Application of Machine Learning in Diagnosis of Pulmonary Disorder
	10.14 Conclusion
	Learning Outcomes
	Exercise for Practice and Discussion
	References
11 Mental Illness and Neurodevelopmental Disorders
	Learning Objectives
	11.1 Neurodevelopmental Disorders
	11.2 Developmental Dyslexia
		11.2.1 Diagnostic Methods
		11.2.2 Behavioural Method
		11.2.3 Brain Imaging Modalities
		11.2.4 Recent Advancement in Diagnostic Techniques
	11.3 Attention-Deficit/Hyperactivity Disorder (ADHD)
		11.3.1 Types
		11.3.2 Symptoms
		11.3.3 ADHD Screening
		11.3.4 Diagnosis Based On Brain Imaging and Machine Learning Methods
		11.3.5 Treatment for ADHD
	11.4 Parkinson’s Disease
		11.4.1 Parkinson’s Disease Prognosis and Measurement Rating Scales
			11.4.1.1 HY Scale
			11.4.1.2 UPDRS Scale
		11.4.2 Involvement of Digital Technologies for Detection and Monitoring of PD
	11.5 Epilepsy
		11.5.1 Recent Literatures On Epilepsy Detection
		11.5.2 Generalized Machine Learning Model for Epilepsy Detection System
	11.6 Schizophrenia
		11.6.1 Recent Research
		11.6.2 A Machine Learning Model for Schizophrenia Detection
	Learning Outcomes
	References
12 Applications and Challenges
	Learning Objectives
	12.1 Role of Machine Learning in Healthcare Research
	12.2 Efficient Diagnosis of Diabetes
	12.3 Neuropathy
	12.4 Drug Monitoring
	12.5 Bioinformatics
	12.6 DNA Analysis
	12.7 Digital Health Records
	12.8 Future Research Challenges
	Learning Outcomes
	References
Index