Machine Learning in Medicine

1. Front Matter

   Pages i-xiv

   PDF

2. Introduction to Machine Learning Part Two

   • Ton J. Cleophas, Aeilko H. Zwinderman

   Pages 1-7

3. Two-Stage Least Squares

   • Ton J. Cleophas, Aeilko H. Zwinderman

   Pages 9-15

4. Multiple Imputations

   • Ton J. Cleophas, Aeilko H. Zwinderman

   Pages 17-26

5. Bhattacharya Analysis

   • Ton J. Cleophas, Aeilko H. Zwinderman

   Pages 27-38

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

   • Ton J. Cleophas, Aeilko H. Zwinderman

   Pages 39-44

7. Logistic Regression for Assessing Novel Diagnostic Tests Against Control

   • Ton J. Cleophas, Aeilko H. Zwinderman

   Pages 45-52

8. Validating Surrogate Endpoints

   • Ton J. Cleophas, Aeilko H. Zwinderman

   Pages 53-64

9. Two-Dimensional Clustering

   • Ton J. Cleophas, Aeilko H. Zwinderman

   Pages 65-75

10. Multidimensional Clustering

    • Ton J. Cleophas, Aeilko H. Zwinderman

    Pages 77-91

11. Anomaly Detection

    • Ton J. Cleophas, Aeilko H. Zwinderman

    Pages 93-103

12. Association Rule Analysis

    • Ton J. Cleophas, Aeilko H. Zwinderman

    Pages 105-113

13. Multidimensional Scaling

    • Ton J. Cleophas, Aeilko H. Zwinderman

    Pages 115-128

14. Correspondence Analysis

    • Ton J. Cleophas, Aeilko H. Zwinderman

    Pages 129-137

15. Multivariate Analysis of Time Series

    • Ton J. Cleophas, Aeilko H. Zwinderman

    Pages 139-153

16. Support Vector Machines

    • Ton J. Cleophas, Aeilko H. Zwinderman

    Pages 155-161

17. Bayesian Networks

    • Ton J. Cleophas, Aeilko H. Zwinderman

    Pages 163-170

18. Protein and DNA Sequence Mining

    • Ton J. Cleophas, Aeilko H. Zwinderman

    Pages 171-185

19. Continuous Sequential Techniques

    • Ton J. Cleophas, Aeilko H. Zwinderman

    Pages 187-194

20. Discrete Wavelet Analysis

    • Ton J. Cleophas, Aeilko H. Zwinderman

    Pages 195-206

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.

• Bayesian networks
• Discrete wavelet analysis
• Protein and DNA sequence mining
• Support vector machines
• Various clustering models
• Entomology

From the reviews:

“This is the second volume of a novel publication on machine learning in medicine that details statistical analysis of complex data with many variables. … The intended audience includes physicians, clinical researchers, physicians in training, statisticians, and medical students as well as master’s and doctoral students in biostatistics and epidemiology. … The simple language and well-organized chapters are unsurpassed attributes of this book. It is an exceptional resource for a quick review of machine learning in medicine.” (Goral Panchal, Doody’s Book Reviews, October, 2013)

• Sliedrecht, Netherlands

  Ton J. Cleophas

• , Dept. Epidemiology and Biostatistics, Academic Medical Center, Amsterdam, Netherlands

  Aeilko H. Zwinderman

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