Statistical Learning for Biomedical Data

Statistical Learning for Biomedical Data

by James D. Malley, Karen G. Malley, Sinisa Pajevic
     
 

ISBN-10: 0521699096

ISBN-13: 9780521699099

Pub. Date: 02/28/2011

Publisher: Cambridge University Press

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional…  See more details below

Overview

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, support vector machines, nearest neighbors and boosting.

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Product Details

ISBN-13:
9780521699099
Publisher:
Cambridge University Press
Publication date:
02/28/2011
Series:
Practical Guides to Biostatistics and Epidemiology Series
Pages:
298
Product dimensions:
6.90(w) x 9.60(h) x 0.80(d)

Related Subjects

Table of Contents

Preface; Acknowledgements; Part I. Introduction: 1. Prologue; 2. The landscape of learning machines; 3. A mangle of machines; 4. Three examples and several machines; Part II. A Machine Toolkit: 5. Logistic regression; 6. A single decision tree; 7. Random forests – trees everywhere; Part III. Analysis Fundamentals: 8. Merely two variables; 9. More than two variables; 10. Resampling methods; 11. Error analysis and model validation; Part IV. Machine Strategies: 12. Ensemble methods – let's take a vote; 13. Summary and conclusions; References; Index.

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