Includes bibliographical references and index.

"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" -- Provided by publisher.

Description based on online resource; title from digital title page (viewed on August 26, 2021).

Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: INTRODUCTION TO INTELLIGENTHEALTHCARE SYSTEMS -- 1 Innovation on Machine Learning in Healthcare Services-An Introduction -- 1.1 Introduction -- 1.2 Need for Change in Healthcare -- 1.3 Opportunities of Machine Learning in Healthcare -- 1.4 Healthcare Fraud -- 1.4.1 Sorts of Fraud in Healthcare -- 1.4.2 Clinical Service Providers -- 1.4.3 Clinical Resource Providers -- 1.4.4 Protection Policy Holders -- 1.4.5 Protection Policy Providers -- 1.5 Fraud Detection and Data Mining in Healthcare -- 1.5.1 Data Mining Supervised Methods -- 1.5.2 Data Mining Unsupervised Methods -- 1.6 Common Machine Learning Applications in Healthcare -- 1.6.1 Multimodal Machine Learning for Data Fusion in Medical Imaging -- 1.6.2 Machine Learning in Patient Risk Stratification -- 1.6.3 Machine Learning in Telemedicine -- 1.6.4 AI (ML) Application in Sedate Revelation -- 1.6.5 Neuroscience and Image Computing -- 1.6.6 Cloud Figuring Systems in Building AI-Based Healthcare -- 1.6.7 Applying Internet of Things and Machine Learning for Personalized Healthcare -- 1.6.8 Machine Learning in Outbreak Prediction -- 1.7 Conclusion -- References -- Part 2: MACHINE LEARNING/DEEP LEARNINGBASEDMODEL DEVELOPMENT -- 2 A Framework for Health Status Estimation Based on Daily Life Activities Data Using Machine Learning Techniques -- 2.1 Introduction -- 2.1.1 Health Status of an Individual -- 2.1.2 Activities and Measures of an Individual -- 2.1.3 Traditional Approach to Predict Health Status -- 2.2 Background -- 2.3 Problem Statement -- 2.4 Proposed Architecture -- 2.4.1 Pre-Processing -- 2.4.2 Phase-I -- 2.4.3 Phase-II -- 2.4.4 Dataset Generation -- 2.4.5 Pre-Processing -- 2.5 Experimental Results -- 2.5.1 Performance Metrics -- 2.6 Conclusion -- References.

3 Study of Neuromarketing With EEG Signals and Machine Learning Techniques -- 3.1 Introduction -- 3.1.1 Why BCI -- 3.1.2 Human-Computer Interfaces -- 3.1.3 What is EEG -- 3.1.4 History of EEG -- 3.1.5 About Neuromarketing -- 3.1.6 About Machine Learning -- 3.2 Literature Survey -- 3.3 Methodology -- 3.3.1 Bagging Decision Tree Classifier -- 3.3.2 Gaussian Na�ive Bayes Classifier -- 3.3.3 Kernel Support Vector Machine (Sigmoid) -- 3.3.4 Random Decision Forest Classifier -- 3.4 System Setup &amp -- Design -- 3.4.1 Pre-Processing &amp -- Feature Extraction -- 3.4.2 Dataset Description -- 3.5 Result -- 3.5.1 Individual Result Analysis -- 3.5.2 Comparative Results Analysis -- 3.6 Conclusion -- References -- 4 An Expert System-Based Clinical Decision Support System for Hepatitis-B Prediction &amp -- Diagnosis -- 4.1 Introduction -- 4.2 Outline of Clinical DSS -- 4.2.1 Preliminaries -- 4.2.2 Types of Clinical DSS -- 4.2.3 Non-Knowledge-Based Decision Support System (NK-DSS) -- 4.2.4 Knowledge-Based Decision Support System (K-DSS) -- 4.2.5 Hybrid Decision Support System (H-DSS) -- 4.2.6 DSS Architecture -- 4.3 Background -- 4.4 Proposed Expert System-Based CDSS -- 4.4.1 Problem Description -- 4.4.2 Rules Set &amp -- Knowledge Base -- 4.4.3 Inference Engine -- 4.5 Implementation &amp -- Testing -- 4.6 Conclusion -- References -- 5 Deep Learning on Symptoms in Disease Prediction -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Mathematical Models -- 5.3.1 Graphs and Related Terms -- 5.3.2 Deep Learning in Graph -- 5.3.3 Network Embedding -- 5.3.4 Graph Neural Network -- 5.3.5 Graph Convolution Network -- 5.4 Learning Representation From DSN -- 5.4.1 Description of the Proposed Model -- 5.4.2 Objective Function -- 5.5 Results and Discussion -- 5.5.1 Description of the Dataset -- 5.5.2 Training Progress -- 5.5.3 Performance Comparisons.

5.6 Conclusions and Future Scope -- References -- 6 Intelligent Vision-Based Systems for Public Safety and Protection via Machine Learning Techniques -- 6.1 Introduction -- 6.1.1 Problems Intended in Video Surveillance Systems -- 6.1.2 Current Developments in This Area -- 6.1.3 Role of AI in Video Surveillance Systems -- 6.2 Public Safety and Video Surveillance Systems -- 6.2.1 Offline Crime Prevention -- 6.2.2 Crime Prevention and Identification via Apps -- 6.2.3 Crime Prevention and Identification via CCTV -- 6.3 Machine Learning for Public Safety -- 6.3.1 Abnormality Behavior Detection via Deep Learning -- 6.3.2 Video Analytics Methods for Accident Classification/Detection -- 6.3.3 Feature Selection and Fusion Methods -- 6.4 Securing the CCTV Data -- 6.4.1 Image/Video Security Challenges -- 6.4.2 Blockchain for Image/Video Security -- 6.5 Conclusion -- References -- 7 Semantic Framework in Healthcare -- 7.1 Introduction -- 7.2 Semantic Web Ontology -- 7.3 Multi-Agent System in a Semantic Framework -- 7.3.1 Existing Healthcare Semantic Frameworks -- 7.3.2 Proposed Multi-Agent-Based Semantic Framework for Healthcare Instance Data -- 7.4 Conclusion -- References -- 8 Detection, Prediction &amp -- Intervention of Attention Deficiency in the Brain Using tDCS -- 8.1 Introduction -- 8.2 Materials &amp -- Methods -- 8.2.1 Subjects and Experimental Design -- 8.2.2 Data Preprocessing &amp -- Statistical Analysis -- 8.2.3 Extracting Singularity Spectrum from EEG -- 8.3 Results &amp -- Discussion -- 8.4 Conclusion -- Acknowledgement -- References -- 9 Detection of Onset and Progression of Osteoporosis Using Machine Learning -- 9.1 Introduction -- 9.1.1 Measurement Techniques of BMD -- 9.1.2 Machine Learning Algorithms in Healthcare -- 9.1.3 Organization of Chapter -- 9.2 Microwave Characterization of Human Osseous Tissue.

9.2.1 Frequency-Domain Analysis of Human Wrist Sample -- 9.2.2 Data Collection and Analysis -- 9.3 Prediction Model of Osteoporosis Using Machine Learning Algorithms -- 9.3.1 K-Nearest Neighbor (KNN) -- 9.3.2 Decision Tree -- 9.3.3 Random Forest -- 9.4 Conclusion -- Acknowledgment -- References -- 10 Applications of Machine Learning in Biomedical Text Processing and Food Industry -- 10.1 Introduction -- 10.2 Use Cases of AI and ML in Healthcare -- 10.2.1 Speech Recognition (SR) -- 10.2.2 Pharmacovigilance and Adverse Drug Effects (ADE) -- 10.2.3 Clinical Imaging and Diagnostics -- 10.2.4 Conversational AI in Healthcare -- 10.3 Use Cases of AI and ML in Food Technology -- 10.3.1 Assortment of Vegetables and Fruits -- 10.3.2 Personal Hygiene -- 10.3.3 Developing New Products -- 10.3.4 Plant Leaf Disease Detection -- 10.3.5 Face Recognition Systems for Domestic Cattle -- 10.3.6 Cleaning Processing Equipment -- 10.4 A Case Study: Sentiment Analysis of Drug Reviews -- 10.4.1 Dataset -- 10.4.2 Approaches for Sentiment Analysis on Drug Reviews -- 10.4.3 BoW and TF-IDF Model -- 10.4.4 Bi-LSTM Model -- 10.4.5 Deep Learning Model -- 10.5 Results and Analysis -- 10.6 Conclusion -- References -- 11 Comparison of MobileNet and ResNet CNN Architectures in the CNN-Based Skin Cancer Classifier Model -- 11.1 Introduction -- 11.2 Our Skin Cancer Classifier Model -- 11.3 Skin Cancer Classifier Model Results -- 11.4 Hyperparameter Tuning and Performance -- 11.4.1 Hyperparameter Tuning of MobileNet-Based CNN Model -- 11.4.2 Hyperparameter Tuning of ResNet50-Based CNN Model -- 11.4.3 Table Summary of Hyperparameter Tuning Results -- 11.5 Comparative Analysis and Results -- 11.5.1 Training and Validation Loss -- 11.5.2 Training and Validation Categorical Accuracy -- 11.5.3 Training and Validation Top 2 Accuracy -- 11.5.4 Training and Validation Top 3 Accuracy.

11.5.5 Confusion Matrix -- 11.5.6 Classification Report -- 11.5.7 Last Epoch Results -- 11.5.8 Best Epoch Results -- 11.5.9 Overall Comparative Analysis -- 11.6 Conclusion -- References -- 12 Deep Learning-Based Image Classifier for Malaria Cell Detection -- 12.1 Introduction -- 12.2 Related Work -- 12.3 Proposed Work -- 12.3.1 Dataset Description -- 12.3.2 Data Pre-Processing and Augmentation -- 12.3.3 CNN Architecture and Implementation -- 12.4 Results and Evaluation -- 12.5 Conclusion -- References -- 13 Prediction of Chest Diseases Using Transfer Learning -- 13.1 Introduction -- 13.2 Types of Diseases -- 13.2.1 Pneumothorax -- 13.2.2 Pneumonia -- 13.2.3 Effusion -- 13.2.4 Atelectasis -- 13.2.5 Nodule and Mass -- 13.2.6 Cardiomegaly -- 13.2.7 Edema -- 13.2.8 Lung Consolidation -- 13.2.9 Pleural Thickening -- 13.2.10 Infiltration -- 13.2.11 Fibrosis -- 13.2.12 Emphysema -- 13.3 Diagnosis of Lung Diseases -- 13.4 Materials and Methods -- 13.4.1 Data Augmentation -- 13.4.2 CNN Architecture -- 13.4.3 Lung Disease Prediction Model -- 13.5 Results and Discussions -- 13.5.1 Implementation Results Using ROC Curve -- 13.6 Conclusion -- References -- 14 Early Stage Detection of Leukemia Using Artificial Intelligence -- 14.1 Introduction -- 14.1.1 Classification of Leukemia -- 14.1.2 Diagnosis of Leukemia -- 14.1.3 Acute and Chronic Stages of Leukemia -- 14.1.4 The Role of AI in Leukemia Detection -- 14.2 Literature Review -- 14.3 Proposed Work -- 14.3.1 Modules Involved in Proposed Methodology -- 14.3.2 Flowchart -- 14.3.3 Proposed Algorithm -- 14.4 Conclusion and Future Aspects -- References -- Part 3: INTERNET OF MEDICAL THINGS (IOMT)FOR HEALTHCARE -- 15 IoT Application in Interconnected Hospitals -- 15.1 Introduction -- 15.2 Networking Systems Using IoT -- 15.3 What are Smart Hospitals? -- 15.3.1 Environment of a Smart Hospital -- 15.4 Assets.

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