Machine Learning in Medicine
Ton J. Cleophas • Aeilko H. Zwinderman
Machine Learning
in Medicine
by
TON J. CLEOPHAS, MD, PhD, Professor,
Past-President American College of Angiology,
Co-Chair Module Statistics Applied to Clinical Trials,
European Interuniversity College of Pharmaceutical Medicine, Lyon, France,
Department Medicine, Albert Schweitzer Hospital, Dordrecht, Netherlands,
AEILKO H. ZWINDERMAN, MathD, PhD, Professor,
President International Society of Biostatistics,
Co-Chair Module Statistics Applied to Clinical Trials,
European Interuniversity College of Pharmaceutical Medicine, Lyon, France,
Department Biostatistics and Epidemiology, Academic Medical Center, Amsterdam,
Netherlands
With the help from
EUGENE P. CLEOPHAS, MSc, BEng,
HENNY I. CLEOPHAS-ALLERS.
1 Introduction to Machine Learning ........................................................ 1
1 Summary ............................................................................................. 1
1.1 Background ............................................................................... 1
1.2 Objective and Methods ............................................................. 1
1.3 Results and Conclusions ........................................................... 1
2 Introduction ......................................................................................... 2
3 Machine Learning Terminology ......................................................... 4
3.1 Arti fi cial Intelligence ................................................................ 4
3.2 Bootstraps ................................................................................. 4
3.3 Canonical Regression ................................................................ 4
3.4 Components .............................................................................. 4
3.5 Cronbach’s alpha ....................................................................... 4
3.6 Cross-Validation ........................................................................ 5
3.7 Data Dimension Reduction ....................................................... 5
3.8 Data Mining .............................................................................. 5
3.9 Discretization ............................................................................ 5
3.10 Discriminant Analysis ............................................................... 5
3.11 Eigenvectors .............................................................................. 6
3.12 Elastic Net Regression .............................................................. 6
3.13 Factor Analysis ......................................................................... 6
3.14 Factor Analysis Theory ............................................................. 6
3.15 Factor Loadings ........................................................................ 7
3.16 Fuzzy Memberships .................................................................. 7
3.17 Fuzzy Modeling ........................................................................ 7
3.18 Fuzzy Plots ................................................................................ 7
3.19 Generalization ........................................................................... 7
3.20 Hierarchical Cluster Analysis ................................................... 7
3.21 Internal Consistency Between the Original Variables
Contributing to a Factor in Factor Analysis .............................. 8
3.22 Iterations ................................................................................... 8
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