Unique features of the book involve the following. 1.This book is the third volume of a three
volume series of cookbooks entitled Machine Learning in Medicine - Cookbooks One Two and
Three. No other self-assessment works for the medical and health care community covering the
field of machine learning have been published to date. 2. Each chapter of the book can be
studied without the need to consult other chapters and can for the readership's convenience
be downloaded from the internet. Self-assessment examples are available at extras.springer.com.
3. An adequate command of machine learning methodologies is a requirement for physicians and
other health workers particularly now because the amount of medical computer data files
currently doubles every 20 months and because soon it will be impossible for them to take
proper data-based health decisions without the help of machine learning. 4. Given the
importance of knowledge of machine learning in the medical and health care community and the
current lack of knowledge of it the readership will consist of any physician and health
worker. 5. The book was written in a simple language in order to enhance readability not only
for the advanced but also for the novices. 6. The book is multipurpose it is an introduction
for ignorant a primer for the inexperienced and a self-assessment handbook for the advanced.
7. The book was particularly written for jaded physicians and any other health care
professionals lacking time to read the entire series of three textbooks. 8. Like the other two
cookbooks it contains technical descriptions and self-assessment examples of 20 important
computer methodologies for medical data analysis and it largely skips the theoretical and
mathematical background. 9. Information of theoretical and mathematical background of the
methods described are displayed in a notes section at the end of eachchapter. 10.Unlike
traditional statistical methods the machine learning methodologies are able to analyze big
data including thousands of cases and hundreds of variables. 11. The medical and health care
community is little aware of the multidimensional nature of current medical data files and
experimental clinical studies are not helpful to that aim either because these studies
usually assume that subgroup characteristics are unimportant as long as the study is
randomized. This is of course untrue because any subgroup characteristic may be vital to an
individual at risk. 12. To date except for a three volume introductary series on the subject
entitled Machine Learning in Medicine Part One Two and Thee 2013 Springer Heidelberg
Germany from the same authors and the current cookbook series no books on machine learning in
medicine have been published. 13. Another unique feature of the cookbooks is that it was
jointly written by two authors from different disciplines one being a clinician clinical
pharmacologist one being a mathematician biostatistician. 14. The authors have also jointly
been teaching at universities and institutions throughout Europe and the USA for the past 20
years. 15. The authors have managed to cover the field of medical data analysis in a
nonmathematical way for the benefit of medical and health workers. 16. The authors already
successfully published many statistics textbooks and self-assessment books e.g. the 67
chapter textbook entitled Statistics Applied to Clinical Studies 5th Edition 2012 Springer
Heidelberg Germany with downloads of 62 826 copies. 17. The current cookbook makes use in
addition to SPSS statistical software of various free calculators from the internet as well
as the Konstanz Information Miner (Knime) a widely approved free machine learning package and
the free Weka Data Mining package from New Zealand. 18. The above software packages with
hundreds of nodes the basic p
