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 & Diagn