Handbook on Intelligent Healthcare Analytics

Figure 6.12Prediction of the closing price using LSTM. Figure 6.13Prediction of the closing price using LR. Figure 6.14Prediction of the closing price using ARIMA. Figure 6.15Prediction of the closing price using LSTM. Figure 6.16Prediction of the closing price using LR. Figure 6.17Prediction of the closing price using ARIMA. Figure 6.18Prediction of the closing price using LSTM. Figure 6.19Prediction of the closing price using LR. Figure 6.20Prediction of the closing price using ARIMA. Figure 6.21Prediction of the closing price using LSTM. Figure 6.22Prediction of the closing price using LR. Figure 6.23Prediction of the closing price using ARIMA. Figure 6.24Prediction of the closing price using LSTM. Figure 6.25Prediction of the closing price using LR. Figure 6.26Prediction of the closing price using ARIMA. Figure 6.27Prediction of the closing price using LSTM. Figure 6.28Prediction of the closing price using LR. Figure 6.29Prediction of the closing price using ARIMA. Figure 6.30Prediction of the closing price using LSTM. Figure 6.31Prediction of the closing price using LR. Figure 6.32Prediction of the closing price using ARIMA. Figure 6.33Prediction of the closing price using LSTM.

7 Chapter 7 Figure 7.1Block diagram for smart diabetes prediction. Figure 7.2Decision tree diagram for attribute age. Figure 7.3Categorized into carbohydrate, protein, and fat. Figure 7.4Percentages of each category of persons identified from analyzed valu... Figure 7.5Conceptual diagram for prediction of ADHD/LD. Figure 7.6Decision tree for classification of learners. Figure 7.7Classification of learners. Figure 7.8Heart disease using naïve bayes classifier. Figure 7.9ECC k(binary) FSM. Figure 7.10k-NAF ECC processor. Figure 7.11k-NAF FSM. Figure 7.12k-NAF ECC FSM. Figure 7.13Battery charge level measurement in Java application using system pr...

8 Chapter 8 Figure 8.1Framework of health recommendation system. Figure 8.2Flowchart of health recommendation system. Figure 8.3Personal information ontology. Figure 8.4SWRL rule for the HRS. Figure 8.5Cases of iris dataset. Figure 8.6Cases of liver disorder.

9 Chapter 9 Figure 9.1Various large data healthcare stakeholders. Figure 9.2Benefits in adopting blockchain healthcare privacy information. Figure 9.3Various forms of big data tools for healthcare. Figure 9.4Electronic medical record (EMR). Figure 9.5Different forms of strategies for security.

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

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

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

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

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

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

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

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

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

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

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

Читать дальше