The current book is the first publication of a complete overview of
machine learning methodologies for the medical and health sector.
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 eighty chapters
eighty different machine learning methodologies are reviewed, in
combination with data examples for self-assessment. Each chapter
can be studied without the need to consult other chapters. The
amount of data stored in the world's databases doubles every 20
months, and clinicians, familiar with traditional statistical
methods, are at a loss to analyze them. Traditional methods have,
indeed, difficulty to identify outliers in large datasets, and to
find patterns in big data and data with multiple exposure / outcome
variables. In addition, analysis-rules for surveys and
questionnaires, which are currently common methods of data
collection, are, essentially, missing. Fortunately, the new
discipline, machine learning, is able to cover all of these
limitations. So far medical professionals have been rather
reluctant to use machine learning. Also, in the field of diagnosis
making, few doctors may want a computer checking them, are
interested in collaboration with a computer or with computer
engineers. Adequate health and health care will, however, soon be
impossible without proper data supervision from modern machine
learning methodologies like cluster models, neural networks and
other data mining methodologies. Each chapter starts with purposes
and scientific questions. Then, step-by-step analyses, using data
examples, are given. Finally, a paragraph with conclusion, and
references to the corresponding sites of three introductory
textbooks, previously written by the same authors, is given.
General
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