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)


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.


Menstrual Cycle Propensity Score Match Generalize Additive Model Menstrual Bleeding Disease Occurrence 
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  1. 1.
    Agresti, A.: Categorical Data Analysis, 2nd edn. Wiley, Chichester (2002)zbMATHGoogle Scholar
  2. 2.
    Royston, P., Altman, D.G., Sauerbrei, W.: Dichotomizing continuous predictors in multiple regression: a bad idea. Stat. Med. 25(1), 127–141 (2006)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Hornik, K., Stinchcombe, M., White, H.: Universal approximation of an unknown mapping and its derivatives using multilayer neural networks. Neural Networks 3, 551–560 (1990)CrossRefGoogle Scholar
  4. 4.
    Hastie, T.I., Tibshirani, R.I.: Generalized Additive models. CRC Press, Boca Raton (1990)zbMATHGoogle Scholar
  5. 5.
    Candiani, G.B., Danesino, V., Gastaldi, A., Parazzini, F., Ferraroni, M.: Reproductive and menstrual factors and risk of peritoneal and ovarian endometriosis. Fertil. Steril. 56, 230–234 (1991)Google Scholar
  6. 6.
    Nisolle, M., Donnez, J.: Peritoneal endometriosis, ovarian endometriosis, and adenomyotic nodules of the rectovaginal septum are three different entities. Fertil. Steril. 68, 585–596 (1997)CrossRefGoogle Scholar
  7. 7.
    Anders, U., Korn, O.: Model selection in neural networks. Neural Networks 12, 309–323 (1999)CrossRefGoogle Scholar
  8. 8.
    Dehejia, R.: Practical propensity score matching. J Econometr. 125(1-2), 355–364 (2005)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Hegland, M., McIntosh, I., Turlach, B.A.: A parallel solver for generalised additive models. Comp. Stat. Data. Anal. 31, 377–396 (1999)zbMATHCrossRefGoogle Scholar

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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