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Machine Learning for Healthcare Applications

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Machine Learning for Healthcare Applications
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Machine Learning for Healthcare Applications: краткое содержание, описание и аннотация

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When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment.
Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning.
This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.

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13 Chapter 14Figure 14.1 Various methods for detecting Leukemia.Figure 14.2 Normal blood and Leukemia infected blood.Figure 14.3 Basic Block diagram of proposed methodology.Figure 14.4 Flowchart of implemented modules.

14 Chapter 15Figure 15.1 Complete layout of the network systems using IoT.Figure 15.2 Network layer of IoT systems.Figure 15.3 List of acronyms and their definitions.Figure 15.4 Smart hospital layout.Figure 15.5 Objectives of smart hospitals.Figure 15.6 Assets of smart hospitals.

15 Chapter 16Figure 16.1 Block diagram of the proposed model.Figure 16.2 Classification using hyperplane.Figure 16.3 Work flow of the health monitoring system.Figure 16.4 Performance analysis using sensitivity.Figure 16.5 Performance analysis using specificity.Figure 16.6 Performance analysis based on accuracy (%).Figure 16.7 Comparative of performance analysis.Figure 16.8 Overview of architecture and interfaces of a system.Figure 16.9 Use case diagram of system design.Figure 16.10 sequence diagram of system design.

16 Chapter 17Figure 17.1 Ontology development and information retrieval process.Figure 17.2 Snippet of concept hierarchy.Figure 17.3 Visualization of concept hierarchy (sample case).Figure 17.4 Information retrieval from knowledgebase.

17 Chapter 18Figure 18.1 Data flow diagram of COVID-19 sentence classification.

18 Chapter 19Figure 19.1 A graph of two class problem with linear separable hyper-plane [21,2...Figure 19.2 Flowchart of the SVM model [23].Figure 19.3 Flowchart of decision tree model [6,24].Figure 19.4 Flowchart of k-means clustering algorithm [6,25].Figure 19.5 Flowchart of levenberg maquardt (LM) training algorithm [6,28].Figure 19.6 Accuracy comparisonFigure 19.7 Training state of levenberg maquardt (LM) method.Figure 19.8 Risk wise classification of other well-known countries.

19 Chapter 20Figure 20.1 Lung CT scan image.Figure 20.2 Crossover.Figure 20.3 Mutation.Figure 20.4 Proposed diagnostic system architecture.Figure 20.5 Original lung CT image.Figure 20.6 Segmentation—Proposed work.Figure 20.7 Lung CT image—segmented.Figure 20.8 ROI segmentation.

20 Chapter 21Figure 21.1 Stages for lung sound prediction.Figure 21.2 The circuit structure of the decomposition.Figure 21.3 Modified random forest architecture for LSS.Figure 21.4 Various performance measures with db4 and MFCC feature extraction of...Figure 21.5 Performance of the Modified-RF, AdaBoost, GB classification algorith...

21 Chapter 22Figure 22.1 Sample sputum smear TB images and its ground truth (taken from Refs....Figure 22.2 Sample bacilli patches used for training and testing. Bacilli images...Figure 22.3 Architecture of the benchmark model (left) and the proposed model (r...Figure 22.4 Training loss and accuracy (mean of 10 experiments) of the proposed ...

22 Chapter 23Figure 23.1 Four patches manually cropped from the image.Figure 23.2 Sixteen sample patches from each class.Figure 23.3 Workflow of the case study for the diagnosis of laryngeal cancer.

23 Chapter 24Figure 24.1 An example of SVM.Figure 24.2 Computer aided diagnosis diagram for diabetic retinopathy detection ...Figure 24.3 Some fundus images.Figure 24.4 Some images of hard exudates and hemorrhages.Figure 24.5 Some images of soft exudates and res small dots.Figure 24.6 Some images of the left eye.Figure 24.7 Some images of the right eye.Figure 24.8 Comparison of accuracy among different classifiers.

Guide

1 Cover

2 Table of Contents

3 Title Page

4 Copyright

5 Preface

6 Begin Reading

7 Index

8 End User License Agreement

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