Abstract
Decision trees are, so-called, non-metric or non-algorithmic methods adequate for fitting nominal and interval data. This chapter is to assess whether decision trees can be appropriately applied to predict health risks and improvements.
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Cleophas, T.J., Zwinderman, A.H. (2014). Decision Trees for Decision Analysis (1,004 and 953 Patients). In: Machine Learning in Medicine - Cookbook. SpringerBriefs in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-04181-0_16
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DOI: https://doi.org/10.1007/978-3-319-04181-0_16
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04180-3
Online ISBN: 978-3-319-04181-0
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