Adequate health and health care is no longer possible without
proper data supervision from modern machine learning methodologies
like cluster models, neural networks, and other data mining
methodologies. The current book is the first publication of a
complete overview of machine learning methodologies for the medical
and health sector, and it was written as a training companion, and
as a must-read, not only for physicians and students, but also for
any one involved in the process and progress of health and health
care. In this second edition the authors have removed the textual
errors from the first edition. Also, the improved tables from the
first edition, have been replaced with the original tables from the
software programs as applied. This is, because, unlike the former,
the latter were without error, and readers were better familiar
with them. The main purpose of the first edition was, to provide
stepwise analyses of the novel methods from data examples, but
background information and clinical relevance information may have
been somewhat lacking. Therefore, each chapter now contains a
section entitled "Background Information". Machine learning may be
more informative, and may provide better sensitivity of testing
than traditional analytic methods may do. In the second edition a
place has been given for the use of machine learning not only to
the analysis of observational clinical data, but also to that of
controlled clinical trials. Unlike the first edition, the second
edition has drawings in full color providing a helpful extra
dimension to the data analysis. Several machine learning
methodologies not yet covered in the first edition, but
increasingly important today, have been included in this updated
edition, for example, negative binomial and Poisson regressions,
sparse canonical analysis, Firth's bias adjusted logistic analysis,
omics research, eigenvalues and eigenvectors.
General
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