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Biomedical Sciences | Machine Learning in Medicine - Part Three

Machine Learning in Medicine

Part Three

Cleophas, Ton J., Zwinderman, Aeilko H.

2013, XIX, 224 p. 41 illus.

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  • Electronic health records of modern health facilities, are increasingly complex and systematic assessment of these records is virtually impossible without special computationally intensive methods
  • Clinicians and other health professionals are not familiar with these methods, and this book is the first publication that systematically reviews such methods, particularly, for this audience
  • The book is written as a hand-hold presentation also accessible to non-mathematicians, and as a must-read publication for those new to the methods
  • The book includes step by step data analyses in SPSS, and can, therefore, also be used as a cookbook-like guide for those starting with the novel methodologies machine learning
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.

Content Level » Upper undergraduate

Related subjects » Biomedical Sciences - Image Processing - Medicine - Statistics

Table of contents 

Preface

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                               

Index    

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