{"paper_id":"f551585a-71ea-451a-a816-53029a9c83ca","body_text":"Summary\nStandard 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.\nPreview\nUnable to display preview. Download preview PDF.\nSimilar content being viewed by others\nReferences\nAgresti, A.: Categorical Data Analysis, 2nd edn. Wiley, Chichester (2002)\nRoyston, P., Altman, D.G., Sauerbrei, W.: Dichotomizing continuous predictors in multiple regression: a bad idea. Stat. Med. 25(1), 127–141 (2006)\nHornik, K., Stinchcombe, M., White, H.: Universal approximation of an unknown mapping and its derivatives using multilayer neural networks. Neural Networks 3, 551–560 (1990)\nHastie, T.I., Tibshirani, R.I.: Generalized Additive models. CRC Press, Boca Raton (1990)\nCandiani, 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)\nNisolle, M., Donnez, J.: Peritoneal endometriosis, ovarian endometriosis, and adenomyotic nodules of the rectovaginal septum are three different entities. Fertil. Steril. 68, 585–596 (1997)\nAnders, U., Korn, O.: Model selection in neural networks. Neural Networks 12, 309–323 (1999)\nDehejia, R.: Practical propensity score matching. J Econometr. 125(1-2), 355–364 (2005)\nHegland, M., McIntosh, I., Turlach, B.A.: A parallel solver for generalised additive models. Comp. Stat. Data. Anal. 31, 377–396 (1999)\nAuthor information\nAuthors and Affiliations\nEditor information\nRights and permissions\nCopyright information\n© 2008 Springer-Verlag Berlin Heidelberg\nAbout this paper\nCite this paper\nRadomski, 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\nDownload citation\nDOI: https://doi.org/10.1007/978-3-540-68168-7_54\nPublisher Name: Springer, Berlin, Heidelberg\nPrint ISBN: 978-3-540-68167-0\nOnline ISBN: 978-3-540-68168-7\neBook Packages: EngineeringEngineering (R0)Springer Nature Proceedings excluding Computer Science\nKeywords\nThese 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.","source_license":"CC0","license_restricted":false}