Machine Learning for Healthcare Applications

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mohanty, Sachi Nandan
Otros Autores: Nalinipriya, G., Jena, Om Prakash, Sarkar, Achyuth
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2021.
Temas:
Medical informatics.
Médecine > Informatique.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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