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Machine Learning in Medicine

Part Three

  • Ton J. Cleophas
  • Aeilko H. Zwinderman

Table of contents

  1. Front Matter
    Pages i-xix
  2. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 1-9
  3. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 11-18
  4. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 19-28
  5. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 29-36
  6. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 37-46
  7. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 47-61
  8. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 63-68
  9. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 69-79
  10. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 81-94
  11. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 95-103
  12. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 105-113
  13. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 115-125
  14. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 127-135
  15. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 137-150
  16. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 151-160
  17. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 161-172
  18. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 173-181
  19. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 183-193
  20. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 195-203
  21. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 205-214
  22. Back Matter
    Pages 215-224

About this book

Introduction

Machine learning is concerned with the analysis of large data and multiple variables. It is also often more sensitive than traditional statistical methods to analyze small data. The first and second volumes reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, fuzzy modeling, various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, association rule learning, anomaly detection, and correspondence analysis. This third volume addresses more advanced methods and includes subjects like evolutionary programming, stochastic methods, complex sampling, optional binning, Newton's methods, decision trees, 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.

Authors and affiliations

  • Ton J. Cleophas
    • 1
  • Aeilko H. Zwinderman
    • 2
  1. 1.Department MedicineAlbert Schweitzer HospitalSliedrechtThe Netherlands
  2. 2.Department Biostatistics and EpidemiologyAcademic Medical CenterAmsterdamThe Netherlands

Bibliographic information