Machine Learning in Medicine
Part Three
Published on 11. December 2013
Book
Hardback
XIX, 224 pages
978-94-007-7868-9 (ISBN)
Description
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.
Reviews / Votes
From the reviews:
"This book is excellent. It is valuable source of a basic understanding of novel machine learning methods of clinical data analysis and can be used as a reference by students and teachers of epidemiology, statistics and biostatistics, computer and social scientists, and clinical investigators." (Vedang J. Bhavsar, Doody's Book Reviews, March, 2014)
More details
Language
English
Place of publication
Dordrecht
Netherlands
Target group
Upper undergraduate
Illustrations
41 s/w Abbildungen
XIX, 224 p. 41 illus.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 19 mm
Weight
535 gr
ISBN-13
978-94-007-7868-9 (9789400778689)
DOI
10.1007/978-94-007-7869-6
Schweitzer Classification
Other editions
Additional editions
Book
04/2017
Springer
€53.49
Shipment within 15-20 days
E-Book
11/2013
Springer
€53.49
Available for download
Persons
The authors are well-qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015), and Professor Cleophas is past-president of the American College of Angiology (2000-2002).
Professor Zwinderman is one of the Principle Investigators of the Academic Medical Center Amsterdam, and his research is concerned with developing statistical methods for new research designs in biomedical science, particularly integrating omics data, like genomics, proteomics, metabolomics, and analysis tools based on parallel computing and the use of cluster computers and grid computing.
Professor Cleophas is a member of the Academic Committee of the European College of Pharmaceutical Medicine, that provides, on behalf of 22 European Universities, the Master-ship trainings "Pharmaceutical Medicine" and "Medicines Development".
From their expertise theyshould be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 18 years, and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.
The authors as professors and teachers in statistics at universities in The Netherlands and France for the most part of their lives, are concerned, that their students find regression-analyses harder than any other methodology in statistics. This is serious, because almost all of the novel methodologies in current data mining and data analysis include elements of regression-analysis, and they do hope that the current production "Regression Analysis for Starters and 2nd Levelers" will be a helpful companion for the purpose. Five textbookscomplementary to the current production and written by the same authors are
Statistics applied to clinical studies 5th edition, 2012, Machine learning in medicine a complete overview, 2015, SPSS for starters and 2nd levelers 2nd edition, 2015, Clinical data analysis on a pocket calculator 2nd edition, 2016, Modern Meta-analysis, 2017Regression Analysis in Medical Research, 2018 all of them published by Springer
Content
Preface.- Introduction to Machine Learning Part Three.- Evolutionary Operations.- Multiple Treatments.- Multiple Endpoints.- Optimal Binning.- Exact P-Values.- Probit Regression.- Over - dispersion.10 Random Effects.- Weighted Least Squares.- Multiple Response Sets.- Complex Samples.- Runs Tests.- Decision Trees.- Spectral Plots.- Newton's Methods.- Stochastic Processes, Stationary Markov Chains.- Stochastic Processes, Absorbing Markov Chains.- Conjoint Models.- Machine Learning and Unsolved Questions.- Index.