Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics
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BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in?? Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics ??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.

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Группа авторов. Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

Table of Contents

List of Illustrations

List of Tables

Guide

Pages

Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics. Concepts, Methodologies, Tools and Applications

Preface. Introduction

Organization of the Book

Concluding Remarks

1. An Introduction to Big Data Analytics Techniques in Healthcare

1.1 Introduction

1.2 Big Data in Healthcare

1.3 Areas of Big Data Analytics in Medicine

1.3.1 Genomics

1.3.2 Signal Processing

1.3.3 Image Processing

1.4 Healthcare as a Big Data Repository

1.5 Applications of Healthcare Big Data

1.5.1 Electronic Health Records (EHRs)

1.5.2 Telemedicine

1.5.3 NoSQL Database

1.5.4 Framework for Reconstructing Epidemiological Dynamics (FRED)

1.5.5 Advanced Risk and Disease Management

1.5.6 Digital Epidemiology

1.5.7 Internet of Things (IoT)

1.5.7.1 IoT for Health Insurance Companies

1.5.7.2 IoT for Physicians

1.5.7.3 IoT for Hospitals

1.5.7.4 IoT for Patients

1.5.8 Improved Supply Chain Management

1.5.9 Developing New Therapies and Innovations

1.6 Challenges in Big Data Analytics

1.7 Big Data Privacy and Security

1.8 Conclusion

1.9 Future Work

References

2. Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia

2.1 Introduction

2.2 Literature Review

2.3 Methodology and Data Source

2.3.1 Study Area

2.3.2 Source of Data

2.3.3 Variables Included in the Study

2.3.4 Building a Predictive Model

2.4 Implementation and Results

2.4.1 Missing Value Handling

2.4.2 Feature Selection Methods

2.4.3 Features Importance Rank

2.4.4 Data Split

2.4.5 Imbalanced Data Handling

2.4.6 Make Predictions on Unseen Test Data

2.4.6.1 Naïve Bayes Classifier: Prediction on Test Data

2.4.6.2 C5.0 Classifier on Train Dataset

2.4.6.3 Rules From Decision Trees

2.4.6.4 SVM Classifier: Unbalanced and Balanced Train Dataset

2.4.6.5 Random Forest Model: On Train Dataset

2.4.7 Evaluation

2.5 Conclusion

References

3. Pre-Trained CNN Models in Early Alzheimer’s Prediction Using Post-Processed MRI

3.1 Introduction

3.1.1 Background

3.2 Experimental Study. 3.2.1 OASIS Longitudinal Data

3.2.1.1 Feature Characteristics

3.2.2 Alzheimer’s 4-Class-MRI-Dataset

3.3 Data Exploration

3.3.1 Features Description

3.4 OASIS Dataset Pre-Processing

3.4.1 Features Selection

3.4.2 Feature Transform

3.4.2.1 MinMaxScaler

3.4.3 Model Selection

3.4.3.1 Decision Tree Classification

3.4.3.2 Ensemble Machine Learning

3.4.3.3 Random Forest Classifier

3.4.4 Model Fitting

3.4.5 Evaluation Metric/Model Evaluation

3.5 Alzheimer’s 4-Class-MRI Features Extraction

3.6 Alzheimer 4-Class MRI Image Dataset. 3.6.1 Image Processing

3.6.2 Classification of 4-CLASS-MRI

3.6.2.1 AlexNet

3.6.2.2 VGG-16

3.6.2.3 Inception (GoogLeNet)

3.6.2.4 Residual Network (“RESNET”)

3.6.2.5 MobileNetV2

3.6.2.6 NASANet (Neural Architecture Search Network)

3.7 RMSProp (Root Mean Square Propagation)

3.8 Activation Function

3.9 Batch Normalization

3.10 Dropout

3.11 Result—I

3.11.1 Result—II

3.12 Conclusion and Future Work

Acknowledgement

References

4. Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging

4.1 Introduction

4.2 Basics of Proposed Methods

4.3 Experimental Results and Discussion

4.4 Conclusion

References

5. Analysis of Healthcare Systems Using Computational Approaches

5.1 Introduction. 5.1.1 Diagnosis Process in Healthcare Systems

5.1.2 Issues of Healthcare

5.1.3 Clinical Diagnosis Based on Soft Computing

5.1.3.1 Neural Network and Fuzzy Healthcare Systems

5.1.3.2 Systems of Fuzzy-Genetic Algorithms (F-GA)

5.1.3.3 Genetic Algorithm Systems and Neural Networks (NNGA)

5.1.3.4 Genetic Algorithm, Fuzzy Logic and Neural Network (NN-FL-GA)

5.1.3.5 Tool for Big Data Analytics

5.2 AI & ML Analysis in Health Systems

5.3 Healthcare Intellectual Approaches

5.3.1 AI and ML Roles in the Healthcare System

5.3.2 Medical ML Medicine

5.3.3 Clinical System Growth

5.3.4 Clinical Data Development Using AI

5.3.5 EHR Disease Detection

5.3.6 Cognitive Cancer Approaches

5.3.7 Effective EHR Operations

5.3.8 Deep Learning Approach (DL) in the Clinical System

5.3.9 Healthcare Data Transformation

5.3.10 Prediction of Cancer

5.4 Precision Approaches to Medicine

5.4.1 EMR Analysis Medicine

5.4.2 AI-Based Medicine Accuracy

5.4.3 Tumor Cell Visual Evaluation

5.5 Methodology of AI, ML With Healthcare Examples

5.6 Big Analytic Data Tools

5.6.1 Hadoop-Based Health Industry Tools

5.6.2 Healthcare System Architecture

5.7 Discussion

5.8 Conclusion

References

6. Expert Systems in Behavioral and Mental Healthcare: Applications of AI in Decision-Making and Consultancy

6.1 Introduction

6.2 AI Methods. 6.2.1 Machine Learning & Artificial Neural Networks (ML & ANN)

6.2.2 Natural Language Processing (NLP)

6.2.3 Machine Perception & Sensing

6.2.4 Affective Computing

6.2.5 Virtual & Augmented Reality (VR & AR)

6.2.6 Cloud Computing & Wireless Technology

6.2.7 Robotics

6.2.8 Brain-Computer Interfaces (BCIs)

6.2.9 Supercomputing & Simulation of Brain

6.3 Turing Test

6.4 Barriers to Technologies

6.5 Advantages of AI for Behavioral & Mental Healthcare

6.6 Enhanced Self-Care & Access to Care

6.6.1 Care Customization

6.6.2 Economic Benefits

6.7 Other Considerations

6.8 Expert Systems in Mental & Behavioral Healthcare

6.8.1 Historical Perspectives

6.9 Dynamical Approaches to Clinical AI and Expert Systems. 6.9.1 Temporal Modeling

6.9.2 Practical Global Clinical Applications

6.9.3 Multi-Agent Model Dedicated to Personalized Medicine

6.9.4 Technology-Enabled Clinicians

6.9.5 Overview of Dynamical Approaches

6.9.6 Cognitive Computing in Healthcare

6.9.7 Emerging Technologies & Clinical AI

6.9.8 Ethics and Futuristic Challenges

6.10 Conclusion

6.11 Future Prospects

References

7. A Mathematical-Based Epidemic Model to Prevent and Control Outbreak of Corona Virus 2019 (COVID-19)

7.1 Introduction. 7.1.1 Corona Viruses

7.1.2 Epidemiological Modeling Using Graph Theory

7.2 Related Work

7.3 Proposed Frameworks. 7.3.1 Infection Spreading Model

7.3.2 Relation between Recovery Time and Interaction of Antivirus Nodes

7.3.3 Proposed Algorithm

7.3.4 Detail Explanation of Algorithm

7.4 Results and Discussion

7.5 Conclusion

References

8. An Access Authorization Mechanism for Electronic Health Records of Blockchain to Sheathe Fragile Information

8.1 Introduction

8.1.1 Basics of Blockchain Technology

8.1.2 Distributed Consensus Protocol

8.1.3 Smart Contracts

8.1.3.1 How Do Smart Contracts Work?

8.1.4 Ethereum and Smart Contracts

8.2 Related Work

8.3 Need for Blockchain in Healthcare

8.4 Proposed Frameworks

8.5 Use Cases

8.6 Discussions

8.7 Challenges and Limitations

8.8 Future Work

8.9 Conclusion

References

9. An Epidemic Graph’s Modeling Application to the COVID-19 Outbreak

9.1 Introduction

9.2 Related Work

9.3 Theoretical Approaches

9.3.1 Graph Convolutional Networks

9.3.2 Recurrent Neural Networks

9.3.3 Epidemic Modeling

9.4 Frameworks

9.4.1 Use the Data Model

9.4.2 Problem Formulation

9.4.3 Proposed Architecture

9.5 Evaluation of COVID-19 Outbreak

9.5.1 Used Datasets

9.5.2 Evolving an Epidemic

9.5.3 Predicted Analysis of the Infected Individuals

9.6 Conclusions and Future Works

References

10. Big Data and Data Mining in e-Health: Legal Issues and Challenges

Object of Study

10.1 Introduction

10.2 Big Data and Data Mining in e-Health

10.3 Big Data and e-Health in India

10.4 Legal Issues Arising Out of Big Data and Data Mining in e-Health

10.4.1 Right to Privacy

10.4.2 Data Privacy Laws

10.4.3 Liability of the Intermediary

10.5 Big Data and Issues of Privacy in e-Health

10.6 Conclusion and Suggestions

References

11. Basic Scientific and Clinical Applications

11.1 Introduction

11.2 Case Study-1: Continual Learning Using ML for Clinical Applications

11.3 Case Study-2

11.4 Case Study-3: ML Will Improve the Radiology Patient Experience

11.5 Case Study-4: Medical Imaging AI with Transition from Academic Research to Commercialization

11.6 Case Study-5: ML will Benefit All Medical Imaging ‘ologies’

11.7 Case Study-6: Health Providers will Leverage Data Hubs to Unlock the Value of Their Data

11.8 Conclusion

References

12. Healthcare Branding Through Service Quality

12.1 Introduction to Healthcare

12.2 Quality in Healthcare

12.2.1 Developing Countries Healthcare Service Quality

12.2.2 Affordability of Quality in Healthcare

12.2.3 Dimensions of Healthcare Service

12.2.4 Healthcare Brand Image

12.2.5 Patients’ Satisfaction

12.2.6 Patients’ Loyalty

12.3 Service Quality

12.3.1 Patient Loyalty with Service Quality in Healthcare

12.3.2 Healthcare Policy

12.4 Conclusion and Road Ahead

References

Index

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Scrivener Publishing

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It is almost certain that the healthcare industry will be changed by how it connects with devices and the physical bodies of people by means of Internet of Things. It has applications in the healthcare industry, as well as being beneficial to patients, family members, physicians and hospitals.

Healthcare devices are rapidly becoming more connected, and thus many approaches are necessary to deal with the various scenarios that may arise from that. A health-monitoring device can be used to assist in insurance under writing and operational tasks, for example, is it possible for insurance companies to leverage that data providing this information will help them detect and evaluate potential clients’ claims of fraud as well as identify those who could benefit from this method of treatment.

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