Emerging technologies for healthcare : Internet of Things and deep learning models /: Internet of Things and deep learning models. (2021)

• Record Type: Book
• Further Information: Note: Edited by Monika Mangla [and five others].
• Editors: Mangla, Monika
• Contents: Preface xvii Part I: Basics of Smart Healthcare 1 1 An Overview of IoT in Health Sectors 3; Sheeba P. S. 1.1 Introduction 3 1.2 Influence of IoT in Healthcare Systems 6 1.2.1 Health Monitoring 6 1.2.2 Smart Hospitals 7 1.2.3 Tracking Patients 7 1.2.4 Transparent Insurance Claims 8 1.2.5 Healthier Cities 8 1.2.6 Research in Health Sector 8 1.3 Popular IoT Healthcare Devices 9 1.3.1 Hearables 9 1.3.2 Moodables 9 1.3.3 Ingestible Sensors 9 1.3.4 Computer Vision 10 1.3.5 Charting in Healthcare 10 1.4 Benefits of IoT 10 1.4.1 Reduction in Cost 10 1.4.2 Quick Diagnosis and Improved Treatment 10 1.4.3 Management of Equipment and Medicines 11 1.4.4 Error Reduction 11 1.4.5 Data Assortment and Analysis 11 1.4.6 Tracking and Alerts 11 1.4.7 Remote Medical Assistance 11 1.5 Challenges of IoT 12 1.5.1 Privacy and Data Security 12 1.5.2 Multiple Devices and Protocols Integration 12 1.5.3 Huge Data and Accuracy 12 1.5.4 Underdeveloped 12 1.5.5 Updating the Software Regularly 12 1.5.6 Global Healthcare Regulations 13 1.5.7 Cost 13 1.6 Disadvantages of IoT 13 1.6.1 Privacy 13 1.6.2 Access by Unauthorized Persons 13 1.7 Applications of IoT 13 1.7.1 Monitoring of Patients Remotely 13 1.7.2 Management of Hospital Operations 14 1.7.3 Monitoring of Glucose 14 1.7.4 Sensor Connected Inhaler 15 1.7.5 Interoperability 15 1.7.6 Connected Contact Lens 15 1.7.7 Hearing Aid 16 1.7.8 Coagulation of Blood 16 1.7.9 Depression Detection 16 1.7.10 Detection of Cancer 17 1.7.11 Monitoring Parkinson Patient 17 1.7.12 Ingestible Sensors 18 1.7.13 Surgery by Robotic Devices 18 1.7.14 Hand Sanitizing 18 1.7.15 Efficient Drug Management 19 1.7.16 Smart Sole 19 1.7.17 Body Scanning 19 1.7.18 Medical Waste Management 20 1.7.19 Monitoring the Heart Rate 20 1.7.20 Robot Nurse 20 1.8 Global Smart Healthcare Market 21 1.9 Recent Trends and Discussions 22 1.10 Conclusion 23 References 23 2 IoT-Based Solutions for Smart Healthcare 25; Pankaj Jain, Sonia F Panesar, Bableen Flora Talwar and Mahesh Kumar Sah 2.1 Introduction 26 2.1.1 Process Flow of Smart Healthcare System 26 2.1.1.1 Data Source 26 2.1.1.2 Data Acquisition 27 2.1.1.3 Data Pre-Processing 27 2.1.1.4 Data Segmentation 28 2.1.1.5 Feature Extraction 28 2.1.1.6 Data Analytics 28 2.2 IoT Smart Healthcare System 29 2.2.1 System Architecture 30 2.2.1.1 Stage 1: Perception Layer 30 2.2.1.2 Stage 2: Network Layer 32 2.2.1.3 Stage 3: Data Processing Layer 32 2.2.1.4 Stage 4: Application Layer 33 2.3 Locally and Cloud-Based IoT Architecture 33 2.3.1 System Architecture 33 2.3.1.1 Body Area Network (BAN) 34 2.3.1.2 Smart Server 34 2.3.1.3 Care Unit 35 2.4 Cloud Computing 35 2.4.1 Infrastructure as a Service (IaaS) 37 2.4.2 Platform as a Service (PaaS) 37 2.4.3 Software as a Service (SaaS) 37 2.4.4 Types of Cloud Computing 37 2.4.4.1 Public Cloud 37 2.4.4.2 Private Cloud 38 2.4.4.3 Hybrid Cloud 38 2.4.4.4 Community Cloud 38 2.5 Outbreak of Arduino Board 38 2.6 Applications of Smart Healthcare System 39 2.6.1 Disease Diagnosis and Treatment 41 2.6.2 Health Risk Monitoring 42 2.6.3 Voice Assistants 42 2.6.4 Smart Hospital 42 2.6.5 Assist in Research and Development 43 2.7 Smart Wearables and Apps 43 2.8 Deep Learning in Biomedical 44 2.8.1 Deep Learning 46 2.8.2 Deep Neural Network Architecture 47 2.8.3 Deep Learning in Bioinformatic 49 2.8.4 Deep Learning in Bioimaging 49 2.8.5 Deep Learning in Medical Imaging 50 2.8.6 Deep Learning in Human-Machine Interface 53 2.8.7 Deep Learning in Health Service Management 53 2.9 Conclusion 55 References 55 3 QLattice Environment and Feyn QGraph Models—A New Perspective Toward Deep Learning 69<br /> Vinayak Bharadi 3.1 Introduction 70 3.1.1 Machine Learning Models 70 3.2 Machine Learning Model Lifecycle 71 3.2.1 Steps in Machine Learning Lifecycle 71 3.2.1.1 Data Preparation 72 3.2.1.2 Building the Machine Learning Model 72 3.2.1.3 Model Training 72 3.2.1.4 Parameter Selection 72 3.2.1.5 Transfer Learning 73 3.2.1.6 Model Verification 73 3.2.1.7 Model Deployment 74 3.2.1.8 Monitoring 74 3.3 A Model Deployment in Keras 75 3.3.1 Pima Indian Diabetes Dataset 75 3.3.2 Multi-Layered Perceptron Implementation in Keras 76 3.3.3 Multi-Layered Perceptron Implementation With Dropout and Added Noise 77 3.4 QLattice Environment 80 3.4.1 Feyn Models 80 3.4.1.1 Semantic Types 82 3.4.1.2 Interactions 83 3.4.1.3 Generating QLattice 83 3.4.2 QLattice Workflow 83 3.4.2.1 Preparing the Data 84 3.4.2.2 Connecting to QLattice 84 3.4.2.3 Generating QGraphs 84 3.4.2.4 Fitting, Sorting, and Updating QGraphs 85 3.4.2.5 Model Evaluation 86 3.5 Using QLattice Environment and QGraph Models for COVID-19 Impact Prediction 87 References 91 4 Sensitive Healthcare Data: Privacy and Security Issues and Proposed Solutions 93; Abhishek Vyas, Satheesh Abimannan and Ren-Hung Hwang 4.1 Introduction 94 4.1.1 Types of Technologies Used in Healthcare Industry 94 4.1.2 Technical Differences Between Security and Privacy 95 4.1.3 HIPAA Compliance 95 4.2 Medical Sensor Networks/Medical Internet of Things/Body Area Networks/WBANs 97 4.2.1 Security and Privacy Issues in WBANs/WMSNs/WMIOTs 101 4.3 Cloud Storage and Computing on Sensitive Healthcare Data 112 4.3.1 Security and Privacy in Cloud Computing and Storage for Sensitive Healthcare Data 114 4.4 Blockchain for Security and Privacy Enhancement in Sensitive Healthcare Data 119 4.5 Artificial Intelligence, Machine Learning, and Big Data in Healthcare and Its Efficacy in Security and Privacy of Sensitive Healthcare Data 122 4.5.1 Differential Privacy for Preserving Privacy of Big Medical Healthcare Data and for Its Analytics 124 4.6 Conclusion 124 References 125 Part II: Employment of Machine Learning in Disease Detection 129 5 Diabetes Prediction Model Based on Machine Learning 131; Ayush Kumar Gupta, Sourabh Yadav, Priyanka Bhartiya and Divesh Gupta 5.1 Introduction 131 5.2 Literature Review 133 5.3 Proposed Methodology 135 5.3.1 Data Accommodation 135 5.3.1.1 Data Collection 135 5.3.1.2 Data Preparation 136 5.3.2 Model Training 138 5.3.2.1 K Nearest Neighbor Classification Technique 139 5.3.2.2 Support Vector Machine 140 5.3.2.3 Random Forest Algorithm 142 5.3.2.4 Logistic Regression 144 5.3.3 Model Evaluation 145 5.3.4 User Interaction 145 5.3.4.1 User Inputs 146 5.3.4.2 Validation Using Classifier Model 146 5.3.4.3 Truth Probability 146 5.4 System Implementation 147 5.5 Conclusion 153 References 153 6 Lung Cancer Detection Using 3D CNN Based on Deep L
• Edition: 1st
• Publisher Details: Hoboken : Wiley-Scrivener
• Publication Date: 2021
• Extent: 1 online resource
• Subjects: 610.285631; Artificial intelligence -- Medical applications; Machine learning; Internet of things -- Health aspects
• Languages: English
• ISBNs: 9781119792321
• Related ISBNs: 9781119791720
• Notes: Note: Description based on CIP data; resource not viewed.
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