Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

8 Chapter 8Figure 8.1 Structure of blockchain.Figure 8.2 Layered approach for stakeholders.Figure 8.3 Data divided among various stakeholders.Figure 8.4 Ethereum Blockchain for patient–doctor interaction.Figure 8.5 Ethereum Blockchain use case for patient, doctor and pharmacy interac...Figure 8.6 Flow of process for reports.Figure 8.7 Drug inventory and supply chain management.Figure 8.8 Workflow of the health insurance companies.Figure 8.9 Flowchart for automated flow of diagnosis.

9 Chapter 9Figure 9.1 Proposed architecture for level visualization.Figure 9.2 Contact rate estimator.Figure 9.3 Epidemiological layer.Figure 9.4 Relative errors of the prediction of the number of infected, recovere...Figure 9.5 Relative error of the number of infected patients compared with SIDAR...Figure 9.6 Illustrates a sharp drop in contact rate over lockdown.Figure 9.7 Analysis of the predicted total number of infected individuals by dec...

10 Chapter 10Figure 10.1 e-Health working process with doctor communicates the prescription t...Figure 10.2 Big Data-Data Processing. Image Source: https://www.congruentsoft.co...

11 Chapter 11Figure 11.1 Potential continual learning algorithms.Figure 11.2 The four stages of drug development, along with Phase IV.Figure 11.3 Drug development cost.Figure 11.4 Stages of ML in radiology.Figure 11.5 Medical imaging AI specialists by clinical application.Figure 11.6 Medical imaging AI specialists by clinical application department wi...Figure 11.7 Digital pathology market maturity and growth forecast.Figure 11.8 Centralized clinical data hubs.

12 Chapter 12Figure 12.1 Conceptual framework from literature review.

1 Chapter 2 Table 2.1 Variables description. Table 2.2 Bivariate logistic regression with AOR to distribution of child alive ... Table 2.3 Ranked Boruta features importance result. Table 2.4 Data imbalance sampling technique result. Table 2.5 Comparing sampling method. Table 2.6 Performance of Naïve Bayes classifier. Table 2.7 Performance of C5.0 classifier. Table 2.8 Confusion Matrix of SVM Imbalanced data. Table 2.9 Performance of SVM on prediction on imbalanced data. Table 2.10 Confusion Matrix for SVM balanced data. Table 2.11 Performance of SVM on prediction on balanced data. Table 2.12 Performance of Random Forest classifier. Table 2.13 10-fold cross-validation on imbalance target data. Table 2.14 10-fold cross-validation on balance target data.

2 Chapter 3Table 3.1 Individual’s demographics and their dementia status.Table 3.2 Basic characteristics’ of MRI with Clinical Dementia Rating (CDR).Table 3.3 Descriptive quantitative variables (n = 373).Table 3.4 Actual vs Predicted.Table 3.5 Accuracies of different algorithms.Table 3.6 Training status.Table 3.7 Comparison of transfer learning pretrained CNN learners.

3 Chapter 4Table 4.1 Comparison of proposed method [18] with other segmentation methods usi...Table 4.2 The results of OC segmentation of proposed method [34] using a differe...

4 Chapter 5Table 5.1 Several soft technologies to fulfil various objectives.Table 5.2 Several examples of AI and ML approaches in healthcare.Table 5.3 Information of cancer disease with approaches and dataset.Table 5.4 Various big data analytics tools with functionalities.

5 Chapter 7Table 7.1 Notations used in the proposed algorithms.

6 Chapter 9Table 9.1 Dataset description.Table 9.2 Regional and provincial data are also presented in Table 9.2.

7 Chapter 11Table 11.1 Adoption index for ‘ologies’.

1 Cover

2 Table of Contents

3 Title Page

4 Copyright

5 Preface

6 Begin Reading

7 Index

8 End User License Agreement

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