Читать книгу Handbook on Intelligent Healthcare Analytics - Группа авторов - Страница 3

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 forms of big data tools for healthcare.Figure 9.4 Electronic medical record (EMR).Figure 9.5 Different forms of strategies for security.

10 Chapter 10Figure 10.1 Different types of data analytics. (a) Percentage (%). (b) Types wit...Figure 10.2 Disease categorization by age.Figure 10.3 Disease categorization by age.Figure 10.4 Challenges in healthcare.

11 Chapter 11Figure 11.1 Schematic representation of computer science subfields.Figure 11.2 Methods of machine learning algorithms.Figure 11.3 Neural network architecture.Figure 11.4 Deep learning architecture with multiple layers.Figure 11.5 Block diagram of the CBIR system.

12 Chapter 12Figure 12.1 Comparative study of number of positive COVID-19 cases in various co...Figure 12.2 Comparison of number of COVID-19 deaths in various countries.Figure 12.3 COVID-19 statistics worldwide based on total cases, recovered, death...Figure 12.4 Architecture of the proposed methodology.Figure 12.5 Complete flow of the proposed methodology.Figure 12.6 Statistics of COVID-19 recovered patients (male).Figure 12.7 Statistics of COVID-19 recovered patients (female).Figure 12.8 Analysis of real time data collected.Figure 12.9 Comparison of various machine learning algorithms.

13 Chapter 13Figure 13.1 Diabetes survey as per the category.Figure 13.2 Diabetes survey as per the age range.Figure 13.3 Architecture diagram of the intelligent system for diabetes.Figure 13.4 Process flow of proposed intelligent system for diabetes.Figure 13.5 Facts for type_one_diabetes.Figure 13.6 Rules for type_one_diabetes.Figure 13.7 Predicted output for type_one_diabetes.Figure 13.8 Intelligent system’s complete output for type_one_diabetes.

14 Chapter 14Figure 14.1 Prediction of breast cancer using machine learning algorithms using ...Figure 14.2 Prediction of breast cancer using machine learning algorithms.Figure 14.3 Mitoses distribution in PCA and K-means algorithm.Figure 14.4 Mitoses distribution in machine learning algorithms.Figure 14.5 Performance comparison of various machine learning algorithms.

15 Chapter 15Figure 15.1 Healthcare data sources.Figure 15.2 Process of data handling.Figure 15.3 Applications of ML.Figure 15.4 Types of learning in ML.Figure 15.5 Example for KNN.Figure 15.6 Categories of hyperplane.Figure 15.7 Process of predictive analytics.

16 Chapter 16Figure 16.1 Data fusion hierarchical framework for big data and IoT devices.Figure 16.2 Proposed architecture TLCA in healthcare ecosystem.Figure 16.3 Comparison of features to calculate the prediction of data fusion ac...Figure 16.4 Data fusion along with sensor fusion using TLCA healthcare system.Figure 16.5 Comparison of IoT devices count based on data aggregation.Figure 16.6 Number of procedure based on hierarchical ecosystem vs frequency.Figure 16.7 Accuracy, precision and recall (%) based on distributed framework.

17 Chapter 17Figure 17.1 Normal cell and Abnormal cell as viewed under microscope. (Courtesy ...Figure 17.2 Neural network architecture.Figure 17.3 The predicted normal red blood cell.Figure 17.4 The graphs of training losses against epoch numbers.

18 Chapter 18Figure 18.1 Deep learning–based absence seizure detection work flow.Figure 18.2 First eight segments of single instances after augmentation.Figure 18.3 Feature extraction process with its parameters.Figure 18.4 Convolution layer output of absence seizure pattern in time and freq...Figure 18.5 Working of GRU-SVM.Figure 18.6 Performance of the classifiers.