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Machine Learning for Healthcare Applications
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Страница 1
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
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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
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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
2.4.4.1 Rules Collection
2.4.4.2
Feature Selection
2.4.4.3 Feature Reduction
2.4.4.4 Dataset Generation From Rules
2.4.4.5 Example
2.4.5 Pre-Processing
2.5 Experimental Results
2.5.1 Performance Metrics
2.5.1.1 Accuracy
2.5.1.2 Precision
2.5.1.3 Recall
2.5.1.4 F1-Score
2.6 Conclusion
References
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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
3.4.1.1 Savitzky–Golay Filter
3.4.1.2 Discrete Wavelet Transform
3.4.2 Dataset Description
3.5 Result 3.5.1 Individual Result Analysis
3.5.2 Comparative Results Analysis
3.6 Conclusion
References
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