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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 is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection, correspondence analysis, 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 »Popular/general
Keywords »Bayesian networks - Discrete wavelet analysis - Protein and DNA sequence mining - Support vector machines - Various clustering models