Machine Learning for Healthcare Applications. Группа авторов

      1  Cover

      2  Title Page

      3  Copyright

      4  Preface

      5  Part 1: INTRODUCTION TO INTELLIGENT HEALTHCARE 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 1.5 Fraud Detection and Data Mining in Healthcare 1.6 Common Machine Learning Applications in Healthcare 1.7 Conclusion References

      6  Part 2: MACHINE LEARNING/DEEP LEARNING-BASED MODEL DEVELOPMENT 2 A Framework for Health Status Estimation Based on Daily Life Activities Data Using Machine Learning Techniques 2.1 Introduction 2.2 Background 2.3 Problem Statement 2.4 Proposed Architecture 2.5 Experimental Results 2.6 Conclusion References 3 Study of Neuromarketing With EEG Signals and Machine Learning Techniques 3.1 Introduction 3.2 Literature Survey 3.3 Methodology 3.4 System Setup & Design 3.5 Result 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 4.3 Background 4.4 Proposed Expert System-Based CDSS 4.5 Implementation & Testing 4.6 Conclusion References 5 Deep Learning on Symptoms in Disease Prediction 5.1 Introduction 5.2 Literature Review 5.3 Mathematical Models 5.4 Learning Representation From DSN 5.5 Results and Discussion 5.6 Conclusions and Future Scope References 6 Intelligent Vision-Based Systems for Public Safety and Protection via Machine Learning Techniques 6.1 Introduction 6.2 Public Safety and Video Surveillance Systems 6.3 Machine Learning for Public Safety 6.4 Securing the CCTV Data 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 Instance Data 7.4 Conclusion References 8 Detection, Prediction & Intervention of Attention Deficiency in the Brain Using tDCS 8.1 Introduction 8.2 Materials & Methods 8.3 Results & Discussion 8.4 Conclusion Acknowledgement References 9 Detection of Onset and Progression of Osteoporosis Using Machine Learning 9.1 Introduction 9.2 Microwave Characterization of Human Osseous Tissue 9.3 Prediction Model of Osteoporosis Using Machine Learning Algorithms 9.4 Conclusion Acknowledgment References