Handbook on Intelligent Healthcare Analytics. Группа авторов

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СКАЧАТЬ 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.

      List of Tables

      1 Chapter 2Table 2.1 Entities from weather forecasting dataset.Table 2.2 Sample dataset for predicting weather forecasting.

      2 Chapter 3Table 3.1 Dimensions of big data.Table 3.2 Big data technologies [12, 14, 26].Table 3.3 Difference between electronic health record and electronic medical rec...Table 3.4 Summary of different sources of healthcare data [13].Table 3.5 Patient health checking devices.

      3 Chapter 6Table 6.1 Comparison of LR, ARIMA, and LSTM of MSE and RMSE.

      4 Chapter 7Table 7.1 Results of learners.

      5 Chapter 8Table 8.1 Dataset with cases.

      6 Chapter 9Table 9.1 Knowledge security laws from numerous countries and organizations.Table 9.2 Blockchain flexibility concerning threats.

      7 Chapter 11Table 11.1 Comparison of different DNN architectures.Table 11.2 Different gaps in CBIR systems [36].

      8 Chapter 12Table 12.1 Characteristics details of COVID-19 in various organs [2].Table 12.2 AI methods and big data for health care sector.

      9 Chapter 14Table 14.1 Performance of the various machine learning algorithms.

      10 Chapter 15Table 15.1 Classification and prediction of customer data set.Table 15.2 Clustering and association of customer data set.

      11 Chapter 16Table 16.1 Classification of healthcare data for accurate multiple data integrit...Table 16.2 Healthcare sample dataset for preprocessing and data fusion.

      12 Chapter 17Table 17.1 Performance estimation for accuracy.

      13 Chapter 18Table 18.1 Seizure detection based pre-processing, input formulation, feature ex...Table 18.2 Details of normal, abnormal, and absence subject.Table 18.3 Schematics of convolution layer.Table 18.4 Schematics of GRU-SVM layers.Table 18.5 p-value of a classification model.

      Guide

      1  Cover СКАЧАТЬ