Machine Learning In Medicine

Introduction

Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection, correspondence analysis, and other subjects.

Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study

(1) Statistics Applied to Clinical Studies 5th Edition 2012,

(2) SPSS for Starters Part One and Two 2012, and

(3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York.

TABLE OF CONTENTS (20 Chapters)

Front Matter

Pages i-xiv

Introduction to Machine Learning Part Two

Pages 1-7

Two-Stage Least Squares

Pages 9-15

Multiple Imputations

Pages 17-26

Bhattacharya Analysis

Pages 27-38

Quality-of-Life (QOL) Assessments with Odds Ratios

Pages 39-44

Logistic Regression for Assessing Novel Diagnostic Tests Against Control

Pages 45-52

Validating Surrogate Endpoints

Pages 53-64

Two-Dimensional Clustering

Pages 65-75

Multidimensional Clustering

Pages 77-91

Anomaly Detection

Pages 93-103

Association Rule Analysis

Pages 105-113

Multidimensional Scaling

Pages 115-128

Correspondence Analysis

Pages 129-137

Multivariate Analysis of Time Series

Pages 139-153

Support Vector Machines

Pages 155-161

Bayesian Networks

Pages 163-170

Protein and DNA Sequence Mining

Pages 171-185

Continuous Sequential Techniques

Pages 187-194

Discrete Wavelet Analysis

Pages 195-206

Machine Learning and Common Sense

Pages 207-211

Back Matter

Pages213-231

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Bibliographic information

DOI: https://doi.org/10.1007/978-94-007-6886-4

Copyright Information: Springer Science+Business Media Dordrecht 2013

Publisher Name: Springer, Dordrecht

eBook Packages: Biomedical and Life Sciences

Print ISBN: 978-94-007-6885-7

Online ISBN: 978-94-007-6886-4