Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
Editors
Contributors
1. Machine Learning in Healthcare: An Introduction
1.1 Introduction
1.2 Machine Learning and Its Basic Workings
1.3 Why Machine Learning?
1.4 Machine Learning Techniques
1.4.1 Supervised Learning
1.4.1.1 Workings of Support Vector Machine Algorithm
1.4.2 Unsupervised Learning
1.4.2.1 Clustering Types
1.5 Understanding the Healthcare Industry
1.6 Applications of ML in Healthcare
1.6.1 Machine Learning in Prognosis
1.6.2 Machine Learning in Diagnosis
1.6.3 Electronic Health Records
1.6.3.1 Electronic Health Records and Machine Learning
1.6.4 Applications of ML in Medical Image Analysis
1.6.5 Machine Learning in Natural Language Processing of Medical Documents and Literature
1.6.6 Machine Learning and Pandemic Combatting
1.6.7 Applications of ML in Pandemic Predictions
1.6.8 Applications of ML in Pandemic Controls
1.7 Conclusion
References
2. A Machine Learning Approach to Identify Personality Traits from Social Media
2.1 Introduction
2.2 Related Works
2.3 Proposed Methodology
2.3.1 Classification Using Random Forest
2.3.2 KNN or K-Nearest Neighbour
2.3.3 SVM or Support Vector Machine
2.3.4 Naïve-Bayes
2.3.5 Long Short Term Memory or LSTM
2.3.6 Convolutional Neural Networks or CNN
2.4 Experimental Result and Performance Evaluation
2.4.1 Data Set Preparation
2.4.1.1 Data Set Visualisation
2.4.2 Pre-processing of Data
2.4.3 Classification
2.4.3.1 Random Forest
2.4.3.2 K-Nearest Neighbour
2.4.3.3 Support Vector Machine
2.4.3.4 Naïve-Bayes
2.4.3.5 Convolutional Neural Network
2.4.3.6 Long Short Term Memory
2.5 Results
2.6 Future Scope
2.7 Conclusion
References
3. Influence of Content Strategies on Community Engagement over the Healthcare-Related Social Media Pages in India
3.1 Introduction
3.2 Literature Review
3.2.1 Social Media
3.2.2 Social Media and Healthcare
3.2.3 Engagement over Social Media
3.3 Method
3.3.1 Variables Operationalization
3.3.2 Model Specification
3.4 Results
3.4.1 Results of Poisson Regression
3.5 Conclusion
3.6 Limitations
References
4. The Impact of Social Media in Fighting Emerging Diseases: A Model-Based Study
4.1 Introduction
4.2 Literature Review
4.3 The Mathematical Model
4.4 Some Preliminary Results
4.4.1 Equilibria
4.4.1.1 Disease-Free Equilibrium
4.4.1.2 Endemic Equilibrium
4.4.2 Hopf Bifurcation at Coexistence
4.5 Numerical Simulations
4.5.1 System Behaviour Changes for A
4.5.2 Dynamical Changes due to k and β
4.5.3 Impact of A on the Infected Population
4.5.4 Impact of k on the Infected Population
4.5.5 Effect of β
4.5.6 Effect of λ
4.5.7 Impact of Treatment (b) on the Infected Population
4.5.8 Two-Parameter Bifurcation Diagram
4.6 Discussion
4.7 Conclusion
References
5. Prediction of Diabetes Mellitus Using Machine Learning
5.1 Introduction
5.2 Machine Learning
5.2.1 Supervised Learning
5.2.2 Unsupervised Learning
5.2.3 Reinforcement Learning
5.3 Literature Review
5.4 Workflow
5.5 Proposed Framework Model
5.6 Methods of Classification and Evaluations
5.6.1 Support Vector Machine
5.6.2 Naïve-Bayes
5.6.3 Decision Tree (DT)
5.7 Symptoms of Type 1 Diabetes and Type 2 Diabetes
5.7.1 The Risk Factors for Type 1 and Type 2 Diabetes
5.8 Results and Discussion
5.9 Conclusion and Future Work
References
6. Spectrogram Image Textural Descriptors for Lung Sound Classification
6.1 Introduction
6.2 Literature Survey
6.3 Proposed Approach for Lung Sound Classification Using Time-Frequency Textural Features
6.4 Pre-processing Techniques
6.4.1 Conventional Spectrogram
6.4.2 Log-Mel Spectrogram
6.4.3 Constant-Q Transform (CQT)
6.5 Feature Extraction, Feature Selection, and Classification
6.5.1 Local Binary Pattern (LBP)
6.5.2 Completed Local Binary Pattern (CLBP)
6.5.3 Local Phase Quantization (LPQ)
6.5.4 Neighbourhood Component Analysis (NCA)
6.5.5 Decision Tree
6.6 Experimental Results and Discussion
6.6.1 Database
6.6.2 Pre-processing
6.6.3 Feature Extraction and Feature Selection
6.6.4 Classification Using Decision Tree and Performance Evaluation
6.7 Conclusion
References
7. Medical Image Analysis Using Machine Learning Techniques: A Systematic Review
7.1 Introduction
7.2 Methodology
7.3 History and Characteristics of Medical Images
7.4 Machine Learning Application in Medical Imaging
7.4.1 Artificial Neural Network
7.4.1.1 Analysis of Previous Methods
7.4.1.2 Proposed Solutions
7.4.1.3 Results
7.4.2 K-Nearest Neighbour Algorithm
7.4.2.1 Analysis of Previous Methods
7.4.2.2 Proposed Solutions
7.4.2.3 Results
7.4.3 Genetic Algorithm
7.4.3.1 Analysis of Previous Methods
7.4.3.2 Proposed Solutions
7.4.3.3 Results
7.4.4 Ant Community Optimization
7.4.4.1 Analysis of Previous Methods
7.4.4.2 Proposed Solutions
7.4.4.3 Results
7.5 Discussion and Conclusions
References
8. Impact of Ensemble-Based Models on Cancer Classification, Its Development, and Challenges
8.1 Introduction
8.2 Types of Ensembles and Their Application on Cancer Classification
8.3 Material and Methods
8.3.1 Data Set Description
8.3.2 Maximum Relevance Minimum Redundancy (MRMR)
8.3.3 Support Vector Machine
8.3.4 Decision Tree
8.3.5 Naïve-Bayes
8.3.6 Neural Network
8.3.7 Logistic Regression
8.3.8 KNN
8.3.9 Proposed Stacking Ensemble Model
8.4 Experimental Results and Discussion
8.4.1 Performance Measure
8.4.2 Results and Discussion
8.5 Conclusion
References
9. Performance Comparison of Different Machine Learning Techniques towards Prevalence of Cardiovascular Diseases (CVDs)
9.1 Introduction
9.2 Literature Review
9.3 Data Pre-processing
9.4 Proposed Methodologies
9.4.1 Support Vector Machine (SVM)
9.4.2 Naïve-Bayes (NB)
9.4.3 Logistic Regression (LR)
9.4.4 Bayesian Regularization Neural Network (BRNN)
9.5 Experimental Results
9.6 Conclusion and Future Scopes of the Study
References
10. Deep Neural Networks in Healthcare Systems
10.1 Introduction
10.2 COVID-19 Evolution and Emergence
10.2.1 COVID-19 Situation Worldwide
10.2.2 COVID-Situation in Bharat
10.3 COVID-19 Detection and Measurement History
10.4 AI in COVID-19 Disease
10.4.1 Artificial Intelligence
10.4.2 Supervised Learning
10.4.3 Unsupervised Learning
10.4.4 Reinforcement Learning
10.5 AI Applications in Fighting against COVID-19
10.5.1 Detection and Diagnosis of COVID-19
10.5.2 Identifying, Tracking, and Predicting the Outbreak
10.5.3 AI for Infodemiology and Infoveillance
10.5.4 AI for Biomedicine and Pharmacotherapy
10.6 Metrics for Deep Learning-Based Analysis
10.7 COVID-19 Detection Techniques
10.7.1 Use of Chest X-rays and CT Images for COVID-19
10.7.2 Image-Based Diagnosis of COVID-19 Using ML
10.7.3 X-Ray Images Utilizing Transfer Learning with CNN
10.7.4 Computer Vision and Radiology for COVID-19 Detection
10.8 Forecasting Models
10.8.1 Big Data
10.8.2 Social Media Data/Other Communication Media Data
10.8.3 Stochastic Theory/Mathematical Models
10.8.4 Data Science and ML Techniques
10.9 Data-Driven Analytical Models of COVID-19
10.9.1 Exponential Model
10.9.2 Logistic Model
10.9.3 SIR Model
10.9.4 MetaWards
10.9.5 SIDARTHE
10.10 Visualizing Trends of COVID-19
10.11 Challenges in Data Collection
10.11.1 Regulation
10.11.2 Data Inefficiency
10.11.3 Privacy
10.12 Conclusion and Future Scope
References
11. Deep Learning and Multimodal Artificial Neural Network Architectures for Disease Diagnosis and Clinical Applications
11.1 Introduction
11.2 Technologies in Healthcare Sector
11.3 Applications of Artificial Intelligence in Healthcare
11.4 Machine Learning Techniques in the Healthcare Sector
11.4.1 Naïve-Bayes Classifier
11.4.2 Support Vector Machines
11.4.3 Decision Trees
11.4.4 Random Forest Classifier
11.4.5 Artificial Neural Networks
11.5 Deep Learning Approach in Healthcare
11.6 Deep Neural Network Architectures in the Medical Field
11.6.1 Convolutional Neural Network
11.6.2 Restricted Boltzmann Machine
11.6.3 Deep Belief Networks
11.6.4 Autoencoder
11.6.5 Recurrent Neural Networks
11.6.6 Long Short Term Memory
11.6.7 Generative Adversarial Nets
11.7 Hybrid Approach of Machine Learning and Deep Learning Techniques
11.8 Mathematical Model of Multimodal Neural Network for Disease Prediction and Labeling
11.9 Conclusion
References
12. A Temporal JSON Model to Represent Big Data in IoT-Based e-Health Systems
12.1 Introduction
12.2 Related Work
12.2.1 IoT, IoMT and IoT-Based e-Health Systems
12.2.2 Machine Learning and Deep Learning in Healthcare Systems
12.2.3 Big Data Modeling
12.2.4 Temporal Database Concepts
12.3 TJeH: Our Temporal JSON Model for e-Health IoT Data
12.3.1 Running Example
12.4 C-TJeH: Our Graphical Conceptual Model for Temporal JSON e-Health IoT Data
12.4.1 Conceptual Modeling of Conventional Aspects of e-Health IoT Data under C-TJeH
12.4.2 Conceptual Modeling of Temporal Aspects of e-Health IoT Data under C-TJeH
12.4.3 Running Example Reprise
12.5 Conclusion
References
13. Use of UAVs in the Prevention, Control and Management of Pandemics
13.1 Introduction
13.1.1 Chapter Organization
13.2 The Use of Drones in the Pandemic Emergency: Pandemic Drones
13.3 Materials and Methods
13.3.1 Delivering Medical Goods
13.3.2 Monitoring Crowd and Flows
13.3.3 Machine Learning Algorithms and Codes Used for the Proposed Automated System
13.3.3.1 IFTTT (If This Then That)
13.3.3.2 YOLO
13.3.3.3 SORT
13.3.3.4 Backpropagation
13.3.3.5 MAC Address
13.3.3.6 Genetic Algorithm
13.4 Case Study
13.4.1 Medical Goods Delivery
13.4.2 Monitoring and Control of Movements and Crowding
13.5 Discussion
13.6 Conclusions
Notes
References
14. Implicit Ontology Changes Driven by Evolution of e-Health IoT Sensor Data in the τOWL Semantic Framework
14.1 Introduction
14.2 Related Work
14.2.1 The τOWL Framework
14.2.2 Ontology-Based IoT e-Health Systems
14.2.3 Machine Learning and Deep Learning in Healthcare Systems
14.3 Implicit Ontology Structure Changes Triggered by Non-Conservative Updates to Ontology Data Instances
14.4 Extending τOWL to Support Non-Conservative Updates of Ontology Instances
14.5 Proof-of-Concept Extension of the τOWL-Manager Tool
14.6 Conclusion
Notes
References
15. Classification of Text Data in Healthcare Systems - A Comparative Study
15.1 Introduction
15.2 Related Work
15.3 Text Classification Algorithms and Techniques
15.3.1 Text Classification Framework
15.3.2 Data Correction
15.3.3 Data Pre-processing
15.3.4 Vectorization of Text Data
15.3.4.1 Bag-of-Words Approach
15.3.4.2 Vector Weighting Techniques
15.3.4.3 Term Frequency (TF)
15.3.4.4 Term Frequency - Inverse Document Frequency (TF-IDF)
15.3.5 Word Embeddings
15.3.5.1 Fasttext
15.3.6 Sampling Methods
15.3.6.1 N-Fold Cross-Validation (N-Fold CV)
15.3.7 Supervised Machine Learning-Based Classifiers
15.3.7.1 Naïve-Bayes (NB)
15.3.7.2 Logistic Regression (LR)
15.3.7.3 Support Vector Machine (SVM)
15.3.7.4 K-Nearest Neighbors (KNN)
15.3.7.5 Decision Tree (DT)
15.3.7.6 Random Forest (RF)
15.3.8 Deep Learning-Based Classifiers
15.3.8.1 Artificial Neural Networks (ANNs)
15.3.9 Pre-Trained Language Models
15.3.9.1 Bidirectional Encoder Representations from Transformers (BERT)
15.3.10 Evaluation Metrics
15.3.10.1 Accuracy
15.3.10.2 Precision
15.3.10.3 Recall
15.3.10.4 F-Measure
15.4 Research Methodology
15.5 Data Set
15.6 Experiments and Results
15.6.1 Computing Setup
15.6.2 Experiments Using Supervised Machine Learning Algorithms
15.6.3 Experiments Using Word Embeddings
15.6.4 Experiments Using Pre-Trained Language Models
15.7 Discussion and Conclusion
References
16. Predicting Air Quality Index with Machine Learning Models
16.1 Introduction
16.2 Related Work
16.3 Model Selection
16.4 Regression Models
16.4.1 Multiple Linear Regression
16.4.2 Decision Tree Regression
16.4.3 Random Forest Regression
16.4.4 Support Vector Regression
16.5 Implementation
16.6 Data Collection
16.7 Missing Value Processing
16.8 Feature Selection
16.9 Data Transformation and Feature Scaling
16.9.1 Error Metrics
16.10 Results and Discussion
16.11 Conclusion
References
Index