Machine Learning in Medicine: Part Three by Ton J. CleophasMachine Learning in Medicine: Part Three by Ton J. Cleophas

Machine Learning in Medicine: Part Three

byTon J. Cleophas, Aeilko H. Zwinderman

Hardcover | December 11, 2013

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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.
Title:Machine Learning in Medicine: Part ThreeFormat:HardcoverDimensions:224 pagesPublished:December 11, 2013Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:9400778686

ISBN - 13:9789400778689

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Table of Contents


1 Introduction to Machine Learning Part Three

2 Evolutionary Operations

3 Multiple Treatments

4 Multiple Endpoints

5 Optimal Binning

6 Exact P-Values

7 Probit Regression

8 Over-dispersion

9 Random Effects

10 Weighted Least Squares

11 Multiple Response Sets

12 Complex Samples

13 Runs Tests

14 Decision Trees

15 Spectral Plots

16 Newton's Methods

17 Stochastic Processes, Stationary Markov Chains

18 Stochastic Processes, Absorbing Markov Chains

19 Conjoint Models

20 Machine Learning and Unsolved Questions


Editorial Reviews

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)