Abstract
The logical combinatorial approach of the pattern recognition theory works with the description of objects in terms of a combination of quantitative and qualitative variables, giving the possibility to consider absent information for the values of some variables in the object description. This approach uses supervised classification algorithms, which are based on the concept of partial precedence, that is, partial analogies (an object can be alike to another object, not in its totality). These characteristics are suitable to model classification problems in Medicine. The objective of this work is to show the usefulness of the logical combinatorial approach to solve problems of pattern recognition in Medicine by illustrating three case studies: the differential diagnosis of Glaucoma, a method for comparing somatotypes (human body types in terms of physical structure), and the prognosis of rehabilitation of patients with cleft lip and palate.
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Ortiz-Posadas, M.R. (2017). The Logical Combinatorial Approach Applied to Pattern Recognition in Medicine. In: Toni, B. (eds) New Trends and Advanced Methods in Interdisciplinary Mathematical Sciences. STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health. Springer, Cham. https://doi.org/10.1007/978-3-319-55612-3_8
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DOI: https://doi.org/10.1007/978-3-319-55612-3_8
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