href="#u6e6e5398-4e8a-4501-9274-ab68f08800bc">17 Commercial Platforms for Healthcare Analytics: Health Issues for Patients with Sickle Cells 17.1 Introduction 17.2 Materials and Methods 17.3 Results and Discussion 17.4 Conclusion References
22 18 New Trends and Applications of Big Data Analytics for Medical Science and Healthcare 18.1 Introduction 18.2 Related Work 18.3 Convolutional Layer 18.4 Pooling Layer 18.5 Fully Connected Layer 18.6 Recurrent Neural Network 18.7 LSTM and GRU 18.8 Materials and Methods 18.9 Results and Discussions 18.10 Conclusion 18.11 Acknowledgement References
23 Index
List of Illustrations
1 Chapter 1Figure 1.1 Knowledge engineering.Figure 1.2 Knowledge as modelling process.Figure 1.3 KBE.
2 Chapter 2Figure 2.1 Traditional Bayesian Neural Network disaster prediction from the data...Figure 2.2 Proposed system for predicting disaster using improved Bayesian hidde...Figure 2.3 Total number of disaster analysis using improved Bayesian Markov chai...Figure 2.4 Changes from various impacts from natural disaster.Figure 2.5 Economic damage changes a prediction analysis.Figure 2.6 Boxplot view of natural disaster on various entity.
3 Chapter 3Figure 3.1 Dimensions of big data.Figure 3.2 Big data value creation flow.Figure 3.3 Different sources of healthcare data.Figure 3.4 Knowledge discovery process of big data in healthcare.
4 Chapter 4Figure 4.1 Architecture diagram.Figure 4.2 Functional block diagram.Figure 4.3 Storage block.Figure 4.4 Reporting block.Figure 4.5 Analysis block.Figure 4.6 Management block.Figure 4.7 Use case diagram.Figure 4.8 Sequence diagram.Figure 4.9 Class diagram.Figure 4.10 Cases of patients.Figure 4.11 Notifications of medicines to endpoints.Figure 4.12 Admin dashboard.
5 Chapter 5Figure 5.1 Process of knowledge engineering.Figure 5.2 Data science and knowledge engineering.
6 Chapter 6Figure 6.1 Conceptual healthcare stock prediction system.Figure 6.2 Overview of business intelligence and analytics framework.Figure 6.3 Illustration of healthcare stock prediction system.Figure 6.4 Prediction of the closing price using LR.Figure 6.5 Prediction of the closing price using ARIMA.Figure 6.6 Prediction of the closing price using LSTM.Figure 6.7 Prediction of the closing price using LR.Figure 6.8 Prediction of the closing price using ARIMA.Figure 6.9 Prediction of the closing price using LSTM.Figure 6.10 Prediction of the closing price using LR.Figure 6.11 Prediction of the closing price using ARIMA.Figure 6.12 Prediction of the closing price using LSTM.Figure 6.13 Prediction of the closing price using LR.Figure 6.14 Prediction of the closing price using ARIMA.Figure 6.15 Prediction of the closing price using LSTM.Figure 6.16 Prediction of the closing price using LR.Figure 6.17 Prediction of the closing price using ARIMA.Figure 6.18 Prediction of the closing price using LSTM.Figure 6.19 Prediction of the closing price using LR.Figure 6.20 Prediction of the closing price using ARIMA.Figure 6.21 Prediction of the closing price using LSTM.Figure 6.22 Prediction of the closing price using LR.Figure 6.23 Prediction of the closing price using ARIMA.Figure 6.24 Prediction of the closing price using LSTM.Figure 6.25 Prediction of the closing price using LR.Figure 6.26 Prediction of the closing price using ARIMA.Figure 6.27 Prediction of the closing price using LSTM.Figure 6.28 Prediction of the closing price using LR.Figure 6.29 Prediction of the closing price using ARIMA.Figure 6.30 Prediction of the closing price using LSTM.Figure 6.31 Prediction of the closing price using LR.Figure 6.32 Prediction of the closing price using ARIMA.Figure 6.33 Prediction of the closing price using LSTM.
7 Chapter 7Figure 7.1 Block diagram for smart diabetes prediction.Figure 7.2 Decision tree diagram for attribute age.Figure 7.3 Categorized into carbohydrate, protein, and fat.Figure 7.4 Percentages of each category of persons identified from analyzed valu...Figure 7.5 Conceptual diagram for prediction of ADHD/LD.Figure 7.6 Decision tree for classification of learners.Figure 7.7 Classification of learners.Figure 7.8 Heart disease using naïve bayes classifier.Figure 7.9 ECC k(binary) FSM.Figure 7.10 k-NAF ECC processor.Figure 7.11 k-NAF FSM.Figure 7.12 k-NAF ECC FSM.Figure 7.13 Battery charge level measurement in Java application using system pr...
8 Chapter 8Figure 8.1 Framework of health recommendation system.Figure 8.2 Flowchart of health recommendation system.Figure 8.3 Personal information ontology.Figure 8.4 SWRL rule for the HRS.Figure 8.5 Cases of iris dataset.Figure 8.6 Cases of liver disorder.
9 Chapter 9Figure 9.1 Various large data healthcare stakeholders.Figure 9.2 Benefits in adopting blockchain healthcare privacy information.Figure 9.3 Various