Machine Learning for Healthcare Applications.

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 Naïve 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.

15.4.1 Overview of Smart Hospital Assets.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.