Machine Learning for Healthcare Applications. Группа авторов

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      1 Chapter 1Figure 1.1 Categorization of healthcare fraud.

      2 Chapter 2Figure 2.1 Architecture of the model.Figure 2.2 Screenshots of the web application.Figure 2.3 Accuracy: Model-I vs Model-II.Figure 2.4 Precision: Model-I vs Model-II.Figure 2.5 Recall: Model-I vs Model-II.Figure 2.6 Recall: Model-I vs Model-II.

      3 Chapter 3Figure 3.1 Brain map structure and Equipment used.Figure 3.2 Workflow diagram.Figure 3.3 DWT schematic.Figure 3.4 Images used for visual evaluation.Figure 3.5 Sample of EEG signal for a product with corresponding Brain map and c...Figure 3.6 Accuracy for all users (compiled).Figure 3.7 Individual result of each algorithm.Figure 3.8 Result of 25-users with different algorithms.Figure 3.9 Result of 25-users compared with different algorithms.Figure 3.10 Approximate brain EEG map for dislike state.Figure 3.11 Approximate brain EEG map for like state.

      4 Chapter 4Figure 4.1 Classification of clinical DSS.Figure 4.2 Architecture of CDSS [29].Figure 4.3 Inference using decision tree for the proposed system.Figure 4.4 (a) First level UI of the system in ES-Builder.Figure 4.4 (b) Second level UI of the system in ES-Builder.Figure 4.4 (c) Third level UI of the system in ES-Builder.Figure 4.4 (d) Fourth level UI of the system in ES-Builder.Figure 4.4 (e) Fifh level UI of the system in ES-Builder.Figure 4.4 (f) Sixth level UI of the system in ES-Builder.Figure 4.4 (g) Conclusion level UI of the system in ES-Builder.

      5 Chapter 5Figure 5.1 Graphs: (a) Euclidean graph, (b) Non-euclidean graph.Figure 5.2 Representation of DSN.Figure 5.3 Training steps of the model.Figure 5.4 Training Performance: (a) Loss, (b) Accuracy.Figure 5.5 Performance comparison: (a) Accuracy, (b) Precision, (c) Recall, (d) ...

      6 Chapter 7Figure 7.1 Sample association between URI, RDF and SPARQL.Figure 7.2 SKCE Multi agent system flowchart.Figure 7.3 Semantic translation framework for healthcare instance data.Figure 7.4 Sample data dictionary with meta classes, concepts and concept values...Figure 7.5 Concept level mappings between different data dictionary elements.Figure 7.6 Sample RDF model of concept level mapping between different data mode...

      7 Chapter 8Figure 8.1 The tDCS montage was 7x5 mm electrodes centred over F3 (connector at ...Figure 8.2 (a) Spectral Decomposition of EEG of Subject 8 which shows bad channe...Figure 8.3 Legendre Spectrum of Subject 8 for Anodal Session.Figure 8.4 ‘Stimuli’ is coded as ‘0’ for subjects given at their first visit to ...Figure 8.5 Relationships between pair of variables, in the form of a 6 × 6 matri...Figure 8.6 (a) While “SESSION” is coded from 1 to 6 for Anodal-Pre, Anodal-DCS, ...Figure 8.7 (a) “Gender” is coded in such a way that ‘0’ denotes a Male and ‘1’ d...

      8 Chapter 9Figure 9.1 Human wrist characterization through the microwave setup.Figure 9.2 Transfer characteristics through the simulated wrist for standard siz...Figure 9.3 Simulated transfer characteristic with healthy bone by varying the bo...Figure 9.4 Simulated transfer characteristic with osteopenia bone by varying the...Figure 9.5 Simulated transfer characteristic with osteoporotic bone 1 by varying...Figure 9.6 Simulated transfer characteristic with osteoporotic bone 2 by varying...Figure 9.7 Simulated transfer characteristic with osteoporotic bone 3 by varying...Figure 9.8 Simulated transfer characteristic with osteoporotic bone 4 by varying...Figure 9.9 Confusion matrix for KNN.Figure 9.10 Confusion matrix for decision tree.Figure 9.11 Confusion matrix for random forest.Figure 9.12 Graphical representation of the classification report.

      9 Chapter 10Figure 10.1 Industry landscape of AI in healthcare (Courtesy: Emily Kuo [2]).Figure 10.2 Tomra—Tomato sorting and processing machines (Courtesy: Tomra [9]).Figure 10.3 Kankan’s Machine system (Courtesy: KanKan AI [11]).Figure 10.4 Plant disease detection (Courtesy: Bitrefine [12]).Figure 10.5 Lameness of domestic cattle (Courtesy: Shearer et al. [13]).Figure 10.6 Processing steps in sentiment analysis.Figure 10.7 Bi-direction LSTM model for text sequence classification.Figure 10.8 Word embedding representation in vector space (Courtesy: David Rozad...Figure 10.9 BERT input embeddings (Courtesy: Cheney [25]).Figure 10.10 Fine-tuning of pre-trained BERT models.Figure 10.11 BERT layered model with classifier (Courtesy: Chris McCormick and N...

      10 Chapter 11Figure 11.1 A snapshot of HAM10000 dataset.Figure 11.2 A high-level view of our classification model.Figure 11.3 Training and validation loss MobileNet.Figure 11.4 Training and validation loss ResNet50.Figure 11.5 Training and validation categorical accuracy MobileNet.Figure 11.6 Training and validation categorical accuracy ResNet50.Figure 11.7 Training and validation top2 accuracy MobileNet.Figure 11.8 Training and validation top2 accuracy ResNet50.Figure 11.9 Training and validation top3 accuracy MobileNet.Figure 11.10 Training and validation top3 accuracy ResNet50.Figure 11.11 Confusion matrix MobileNet.Figure 11.12 Confusion matrix ResNet50.Figure 11.13 Classification reports MobileNet.Figure 11.14 Classification reports ResNet50.Figure 11.15 Last epoch results MobileNet.Figure 11.16 Last epoch results ResNet50.Figure 11.17 Best epoch results MobileNet.Figure 11.18 Best epoch results ResNet50.

      11 Chapter СКАЧАТЬ