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2013 | 35 | 1 | 103-115

Article title

Classification Issue in the IVF ICSI/ET Data Analysis: Early Treatment Outcome Prognosis

Title variants

Languages of publication

EN

Abstracts

EN
Infertility is a serious social problem. Very often the only treatment possibility are IVF methods. This study explores the possibility of outcome prediction in the early stages of treatment. The data, collected from the previous treatment cycles, were divided into four subsets, which corresponded to the selected stages of treatment. On each such subset, sophisticated data mining analysis was carried out, with appropriate imputations and classification procedures. The obtained results indicate that there is a possibility of predicting the final outcome at the beginning of treatment.

Keywords

Publisher

Year

Volume

35

Issue

1

Pages

103-115

Physical description

Dates

published
2013-12-01
online
2013-12-31

Contributors

  • Department of Statistics and Medical Informatics, Medical University of Bialystok, Poland
  • Department of Statistics and Medical Informatics, Medical University of Bialystok, Poland
  • Department of Statistics and Medical Informatics, Medical University of Bialystok, Poland
  • Department of Statistics and Medical Informatics, Medical University of Bialystok, Poland
  • Department of Biology and Pathology of Human Reproduction, Institute of Animal Reproduction and Food Research Polish Academy of Sciences, Olsztyn, Poland
  • Department of Reproduction and Gynaecological Endocrinology, Medical University of Bialystok, Poland

References

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  • Milewski, R., Malinowski, P., Milewska, A. J., Ziniewicz, P., Czerniecki, J., Pierzyński, P., & Wołczyński S. (2012). Classification issue in the IVF ICSI/ET data analysis. Studies in Logic, Grammar and Rhetoric, 29(42), 75-85.
  • Milewski, R., Malinowski, P., Milewska, A. J., Czerniecki, J., Ziniewicz, P., & Wołczyński, S. (2011). Nearest neighbor concept in the study of IVF ICSI/ET treatment effectiveness. Studies in Logic, Grammar and Rhetoric, 25(38), 49-57.
  • Milewski, R., Malinowski, P., Milewska, A. J., Ziniewicz, P., & Wołczyński, S. (2010). The usage of margin-based feature selection algorithm in IVF ICSI/ET data analysis. Studies in Logic, Grammar and Rhetoric, 21(34), 35-46.
  • Milewski, R., Milewska, A. J., Czerniecki, J., Leśniewska, M., & Wołczyński, S. (2013). Analysis of the demographic profile of patients treated for infertility using assisted reproductive techniques in 2005-2010. Ginekologia Polska, 84(7), 609-614.
  • Milewski, R., Milewska, A. J., Domitrz, J., & Wołczyński, S. (2008). In vitro fertilization ICSI/ET in women over 40. Przegląd Menopauzalny, 7(2), 85-90.
  • Oba, S., Sato, M., Takemasa, I., Monden, M., Matsubara, K., & Ishii, S. (2003). A Bayesian missing value estimation method for gene expression profile data. Bioinformatics, 19(16), 2088-2096.[Crossref][PubMed]
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  • Stekhoven, D. J., & B¨uhlmann, P. (2012b). missForest: Nonparametric Missing Value Imputation using Random Forest R package version 1.3. Retrieved from http://CRAN.R-project.org/package=missForest.
  • Templ, M., Alfons, A., Kowarik, A. & Prantner, B. (2013). VIM: Visualization and Imputation of Missing Values. R package version 3.0.3.1. Retrieved from http://CRAN.R-project.org/package=VIM.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.doi-10_2478_slgr-2013-0034
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