Machine Learning in Medicine
(Sprache: Englisch)
This handy guide will help clinicians with computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It includes step by step data analyses in SPSS.
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This handy guide will help clinicians with computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It includes step by step data analyses in SPSS.
Klappentext zu „Machine Learning in Medicine “
Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods.
Inhaltsverzeichnis zu „Machine Learning in Medicine “
Preface.- 1 Introduction to machine learning.- 2 Logistic regression for health profiling.- 3 Optimal scaling: discretization.- 4 Optimal scaling: regularization including ridge, lasso, and elastic net regression.- 5 Partial correlations.- 6 Mixed linear modelling.- 7 Binary partitioning.- 8 Item response modelling.- 9 Time-dependent predictor modelling.- 10 Seasonality assessments.- 11 Non-linear modelling.- 12 Artificial intelligence, multilayer Perceptron modelling.- 13 Artificial intelligence, radial basis function modelling.- 14 Factor analysis.- 15 Hierarchical cluster analysis for unsupervised data.- 16 Partial least squares.- 17 Discriminant analysis for Supervised data.- 18 Canonical regression.- 19 Fuzzy modelling.- 20 Conclusions. Index.
Bibliographische Angaben
- Autoren: Ton J. Cleophas , Aeilko H. Zwinderman
- 2015, 2013., 265 Seiten, Maße: 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer Netherlands
- ISBN-10: 9400793634
- ISBN-13: 9789400793637
Sprache:
Englisch
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