| Description |
1 online resource (416 p.) |
| Contents |
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 & Design |
|
3.4.1 Pre-Processing & 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 & 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 |
| Notes |
Description based upon print version of record |
|
4.3 Background |
| Subject |
Medical informatics.
|
| Form |
Electronic book
|
| Author |
Nalinipriya, G
|
|
Jena, Om Prakash
|
|
Sarkar, Achyuth
|
| ISBN |
9781119792604 |
|
1119792606 |
|
