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Machine Learning in Healthcare Informatics

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Cover of 'Machine Learning in Healthcare Informatics'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Machine Learning in Healthcare Informatics
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    Chapter 2 Wavelet-based Machine Learning Techniques for ECG Signal Analysis
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    Chapter 3 Application of Fuzzy Logic Control for Regulation of Glucose Level of Diabetic Patient
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    Chapter 4 The Application of Genetic Algorithm for Unsupervised Classification of ECG
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    Chapter 5 Pixel-based Machine Learning in Computer-Aided Diagnosis of Lung and Colon Cancer
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    Chapter 6 Understanding Foot Function During Stance Phase by Bayesian Network Based Causal Inference
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    Chapter 7 Rule Learning in Healthcare and Health Services Research
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    Chapter 8 Machine Learning Techniques for AD/MCI Diagnosis and Prognosis
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    Chapter 9 Using Machine Learning to Plan Rehabilitation for Home Care Clients: Beyond “Black-Box” Predictions
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    Chapter 10 Clinical Utility of Machine Learning and Longitudinal EHR Data
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    Chapter 11 Rule-based Computer Aided Decision Making for Traumatic Brain Injuries
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    Chapter 12 Supervised Learning Methods for Fraud Detection in Healthcare Insurance
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    Chapter 13 Feature Extraction by Quick Reduction Algorithm: Assessing the Neurovascular Pattern of Migraine Sufferers from NIRS Signals
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    Chapter 14 A Selection and Reduction Approach for the Optimization of Ultrasound Carotid Artery Images Segmentation
Attention for Chapter 12: Supervised Learning Methods for Fraud Detection in Healthcare Insurance
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Chapter title
Supervised Learning Methods for Fraud Detection in Healthcare Insurance
Chapter number 12
Book title
Machine Learning in Healthcare Informatics
Published by
Springer, Berlin, Heidelberg, January 2014
DOI 10.1007/978-3-642-40017-9_12
Book ISBNs
978-3-64-240016-2, 978-3-64-240017-9

Prerna Dua, Sonali Bais

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 17%
Researcher 6 11%
Lecturer 3 6%
Student > Doctoral Student 3 6%
Student > Ph. D. Student 3 6%
Other 9 17%
Unknown 21 39%
Readers by discipline Count As %
Computer Science 9 17%
Business, Management and Accounting 7 13%
Economics, Econometrics and Finance 5 9%
Engineering 3 6%
Decision Sciences 2 4%
Other 6 11%
Unknown 22 41%