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.
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.
Über den Autor
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 [...]opean 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, 2017
Regression Analysis in Medical Research, 2018
all of them published by Springer
Zusammenfassung
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
Inhaltsverzeichnis
Preface1 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
