Preface xvii Part 1: Introduction to Intelligent Healthcare Systems 1 1 Innovation on Machine Learning in Healthcare Services--An Introduction 3 Parthasarathi Pattnayak and Om Prakash Jena 1.1 Introduction 3 1.2 Need for Change in Healthcare 5 1.3 Opportunities of Machine Learning in Healthcare 6 1.4 Healthcare Fraud 7 1.4.1 Sorts of Fraud in Healthcare 7 1.4.
2 Clinical Service Providers 8 1.4.3 Clinical Resource Providers 8 1.4.4 Protection Policy Holders 8 1.4.5 Protection Policy Providers 9 1.5 Fraud Detection and Data Mining in Healthcare 9 1.
5.1 Data Mining Supervised Methods 10 1.5.2 Data Mining Unsupervised Methods 10 1.6 Common Machine Learning Applications in Healthcare 10 1.6.1 Multimodal Machine Learning for Data Fusion in Medical Imaging 11 1.6.
2 Machine Learning in Patient Risk Stratification 11 1.6.3 Machine Learning in Telemedicine 11 1.6.4 AI (ML) Application in Sedate Revelation 12 1.6.5 Neuroscience and Image Computing 12 1.6.
6 Cloud Figuring Systems in Building AI-Based Healthcare 12 1.6.7 Applying Internet of Things and Machine-Learning for Personalized Healthcare 12 1.6.8 Machine Learning in Outbreak Prediction 13 1.7 Conclusion 13 References 14 Part 2: Machine Learning/Deep Learning-Based Model Development 17 2 A Framework for Health Status Estimation Based on Daily Life Activities Data Using Machine Learning Techniques 19 Tene Ramakrishnudu, T. Sai Prasen and V. Tharun Chakravarthy 2.
1 Introduction 19 2.1.1 Health Status of an Individual 19 2.1.2 Activities and Measures of an Individual 20 2.1.3 Traditional Approach to Predict Health Status 20 2.2 Background 20 2.
3 Problem Statement 21 2.4 Proposed Architecture 22 2.4.1 Pre-Processing 22 2.4.2 Phase-I 23 2.4.3 Phase-II 23 2.
4.4 Dataset Generation 23 2.4.4.1 Rules Collection 23 2.4.4.2 Feature Selection 24 2.
4.4.3 Feature Reduction 24 2.4.4.4 Dataset Generation From Rules 24 2.4.4.
5 Example 24 2.4.5 Pre-Processing 26 2.5 Experimental Results 27 2.5.1 Performance Metrics 27 2.5.1.
1 Accuracy 27 2.5.1.2 Precision 28 2.5.1.3 Recall 28 2.5.
1.4 F1-Score 30 2.6 Conclusion 31 References 31 3 Study of Neuromarketing With EEG Signals and Machine Learning Techniques 33 S. Pal, P. Das, R. Sahu and S.R. Dash 3.
1 Introduction 34 3.1.1 Why BCI 34 3.1.2 Human-Computer Interfaces 34 3.1.3 What is EEG 35 3.1.
4 History of EEG 35 3.1.5 About Neuromarketing 35 3.1.6 About Machine Learning 36 3.2 Literature Survey 36 3.3 Methodology 45 3.3.
1 Bagging Decision Tree Classifier 45 3.3.2 Gaussian Naïve Bayes Classifier 45 3.3.3 Kernel Support Vector Machine (Sigmoid) 45 3.3.4 Random Decision Forest Classifier 46 3.4 System Setup & Design 46 3.
4.1 Pre-Processing & Feature Extraction 47 3.4.1.1 Savitzky-Golay Filter 47 3.4.1.2 Discrete Wavelet Transform 48 3.
4.2 Dataset Description 49 3.5 Result 49 3.5.1 Individual Result Analysis 49 3.5.2 Comparative Results Analysis 52 3.6 Conclusion 53 References 54 4 An Expert System-Based Clinical Decision Support System for Hepatitis-B Prediction & Diagnosis 57 Niranjan Panigrahi, Ishan Ayus and Om Prakash Jena 4.
1 Introduction 57 4.2 Outline of Clinical DSS 59 4.2.1 Preliminaries 59 4.2.2 Types of Clinical DSS 60 4.2.3 Non-Knowledge-Based Decision Support System (NK-DSS) 60 4.
2.4 Knowledge-Based Decision Support System (K-DSS) 62 4.2.5 Hybrid Decision Support System (H-DSS) 64 4.2.6 DSS Architecture 64 4.3 Background 65 4.4 Proposed Expert System-Based CDSS 65 4.
4.1 Problem Description 65 4.4.2 Rules Set & Knowledge Base 66 4.4.3 Inference Engine 66 4.5 Implementation & Testing 66 4.6 Conclusion 73 References 73 5 Deep Learning on Symptoms in Disease Prediction 77 Sheikh Raul Islam, Rohit Sinha, Santi P.
Maity and Ajoy Kumar Ray 5.1 Introduction 77 5.2 Literature Review 78 5.3 Mathematical Models 79 5.3.1 Graphs and Related Terms 80 5.3.2 Deep Learning in Graph 80 5.
3.3 Network Embedding 80 5.3.4 Graph Neural Network 81 5.3.5 Graph Convolution Network 82 5.4 Learning Representation From DSN 82 5.4.
1 Description of the Proposed Model 83 5.4.2 Objective Function 84 5.5 Results and Discussion 84 5.5.1 Description of the Dataset 85 5.5.2 Training Progress 85 5.
5.3 Performance Comparisons 86 5.6 Conclusions and Future Scope 86 References 87 6 Intelligent Vision-Based Systems for Public Safety and Protection via Machine Learning Techniques 89 Rajitha B. 6.1 Introduction 89 6.1.1 Problems Intended in Video Surveillance Systems 90 6.1.
2 Current Developments in This Area 91 6.1.3 Role of AI in Video Surveillance Systems 91 6.2 Public Safety and Video Surveillance Systems 92 6.2.1 Offline Crime Prevention 92 6.2.2 Crime Prevention and Identification via Apps 92 6.
2.3 Crime Prevention and Identification via CCTV 92 6.3 Machine Learning for Public Safety 94 6.3.1 Abnormality Behavior Detection via Deep Learning 95 6.3.2 Video Analytics Methods for Accident Classification/Detection 97 6.3.
3 Feature Selection and Fusion Methods 98 6.4 Securing the CCTV Data 99 6.4.1 Image/Video Security Challenges 99 6.4.2 Blockchain for Image/Video Security 99 6.5 Conclusion 99 References 100 7 Semantic Framework in Healthcare 103 Sankar Pariserum Perumal, Ganapathy Sannasi, Selvi M. and Kannan Arputharaj 7.
1 Introduction 103 7.2 Semantic Web Ontology 104 7.3 Multi-Agent System in a Semantic Framework 106 7.3.1 Existing Healthcare Semantic Frameworks 107 7.3.1.1 AOIS 107 7.
3.1.2 SCKE 108 7.3.1.3 MASE 109 7.3.1.
4 MET4 110 7.3.2 Proposed Multi-Agent-Based Semantic Framework for Healthcare Instance Data 111 7.3.2.1 Data Dictionary 111 7.3.2.
2 Mapping Database 112 7.3.2.3 Decision Making Ontology 113 7.3.2.4 STTL and SPARQL-Based RDF Transformation 115 7.3.
2.5 Query Optimizer Agent 116 7.3.2.6 Semantic Web Services Ontology 116 7.3.2.7 Web Application User Interface and Customer Agent 116 7.
3.2.8 Translation Agent 117 7.3.2.9 RDF Translator 117 7.4 Conclusion 118 References 119 8 Detection, Prediction & Intervention of Attention Deficiency in the Brain Using tDCS 121 Pallabjyoti Kakoti, Rissnalin Syiemlieh and Eeshankur Saikia 8.1 Introduction 121 8.
2 Materials & Methods 123 8.2.1 Subjects and Experimental Design 123 8.2.2 Data Pre-Processing & Statistical Analysis 125 8.2.3 Extracting Singularity Spectrum from EEG 126 8.3 Results & Discussion 126 8.
4 Conclusion 132 Acknowledgement 133 References 133 9 Detection of Onset and Progression of Osteoporosis Using Machine Learning 137 Shilpi Ruchi Kerketta and Debalina Ghosh 9.1 Introduction 137 9.1.1 Measurement Techniques of BMD 138 9.1.2 Machine Learning Algorithms in Healthcare 138 9.1.3 Organization of Chapter 139 9.
2 Microwave Characterization of Human Osseous Tissue 139 9.2.1 Frequency-Domain Analysis of Human Wrist Sample 140 9.2.2 Data Collection and Analysis 141 9.3 Prediction Model of Osteoporosis Using Machine Learning Algorithms 144 9.3.1 K-Nearest Neighbor (KNN) 144 9.
3.2 Decision Tree 145 9.3.3 Random Forest 145 9.4 Conclusion 148 Acknowledgment 148 References 148 10 Applications of Machine Learning in Biomedical Text Processing and Food Industry 151 K. Paramesha, Gururaj H.L. and Om Prakash Jena 10.
1 Introduction 152 10.2 Use Cases of AI and ML in Healthcare 153 10.2.1 Speech Recognition (SR) 153 10.2.2 Pharmacovigilance and Adverse Drug Effects (ADE) 153 10.2.3 Clinical Imaging and Diagnostics 153 10.
2.4 Conversational AI in Healthcare 154 10.3 Use Cases of AI and ML in Food Technology 154 10.3.1 Assortment of Vegetables and Fruits 154 10.3.2 Personal Hygiene 154 10.3.
3 Developing New Products 155 10.3.4 Plant Leaf Disease Detection 156 10.3.5 Face Recognition Systems for Domestic Cattle 156 10.3.6 Cleaning Processing Equipment 157 10.4 A Case Study: Sentiment Analysis of Drug Reviews 158 10.
4.1 Dataset 159 10.4.2 Approaches for Sentiment Analysis on Drug Reviews 159 10.4.3 BoW and TF-IDF Model 160 10.4.4 Bi-LSTM Model 160 10.
4.4.1 Word Embedding 160 10.4.5 Deep Learning Model 161 10.5 Results and Analysis 164 10.6 Conclusion 165 References 166 11 Comparison of MobileNet and ResNet CNN Architectures in the CNN-Based Skin Cancer Classifier Model 169 Subasish Mohapatra, N.V.
S. Abhishek, Dibyajit Bardhan, Anisha Ankita Ghosh and Shubhadarshinin Mohanty 11.1 Introduction 169 11.2 Our Skin Cancer Classifier Model 171 11.3 Skin Cancer Classifier Model Results 172 11.4 Hyperparameter Tuning and Performance 174 11.4.1 Hyperparameter Tuning of MobileNet-Based CNN Model 175.