Summary
Standard methods used for an identification of risk factors are based on logistic regression models. These models disabled to assessment a nonlinearity between a study factors and a disease occurrence. This paper presents an application of generalized additive models for modeling of reproductive risk factors associated with endometrioid cysts. Moreover theoretical similarity and differences between generalized additive models and neural networks was discussed. The obtained results enabled to propose a new etiological aspect for endometrioid cysts.
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Radomski, D., Lewandowski, Z., Roszkowski, P.I. (2008). An Application of a Generalized Additive Model for an Identification of a Nonlinear Relation between a Course of Menstrual Cycles and a Risk of Endometrioid Cysts. In: Pietka, E., Kawa, J. (eds) Information Technologies in Biomedicine. Advances in Soft Computing, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68168-7_54
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DOI: https://doi.org/10.1007/978-3-540-68168-7_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68167-0
Online ISBN: 978-3-540-68168-7
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