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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

  • Dariusz Radomski
  • Zbigniew Lewandowski
  • Piotr I. Roszkowski
Part of the Advances in Soft Computing book series (AINSC, volume 47)

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.

Keywords

Menstrual Cycle Propensity Score Match Generalize Additive Model Menstrual Bleeding Disease Occurrence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dariusz Radomski
    • 1
  • Zbigniew Lewandowski
    • 2
  • Piotr I. Roszkowski
    • 3
  1. 1.Division of Nuclear and Medical Electronics, Institute of RadioelectronicsWarsawPoland
  2. 2.Department of Epidemiology MedicalUniversity of WarsawWarsawPoland
  3. 3.Department of Obstetrics and GynecologyMedical University of WarsawWarsawPoland

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