Abstract
Fuzzy modeling is a methodology that works with partial truths: it can answer questions to which the answers are “yes” and “no” at different times or partly “yes” and “no” at the same time. It can be used to match any type of data, particularly incomplete and imprecise data, and it is able to improve the precision of testing of such data. It can be applied with any type of statistical distribution, and it is, particularly, suitable for uncommon and unexpected non linear relationships. This chapter assesses the use of fuzzy modeling of clinical data.
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© 2016 Springer International Publishing Switzerland
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Cleophas, T.J., Zwinderman, A.H. (2016). Fuzzy Modeling for Imprecise and Incomplete Data. In: Clinical Data Analysis on a Pocket Calculator. Springer, Cham. https://doi.org/10.1007/978-3-319-27104-0_29
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DOI: https://doi.org/10.1007/978-3-319-27104-0_29
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27103-3
Online ISBN: 978-3-319-27104-0
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