Significance of Discriminant Analysis in Prediction of Pregnancy in IVF Treatment

• Authors: Anna Justyna Milewska , Dorota Jankowska , Urszula Cwalina , Dorota Citko , Teresa Więsak , Brian Acacio , Robert Milewski
• Languages of publication: EN

Abstracts

EN

Many factors play an important role in prediction of infertility treatment outcome (for example, female age and quality of oocytes or embryos are the most important prognostic factors concerning positive IVF outcome). The purpose of this study was to identify a set of variables that could fulfill criteria for prediction of pregnancy in IVF patients through the application of data mining – using the discriminant analysis method. The principle of this method is to establish a set of rules that allows one to place multi-dimensional objects into one of two analyzed groups (pregnant or not pregnant). Six hundred and ten IVF cycles were included in the analysis and the following variables were taken into consideration: female age, number and quality of retrieved oocytes, number and quality of embryos, number of transferred embryos, and outcome of treatment. Discriminant analysis allowed for the creation of a model with a 51.22% correctness of prediction to achieve pregnancy during IVF treatment and with 74.07% correctly predicted failure of pregnancy. Therefore, the created model is more suitable for the prediction of a negative outcome (lack of pregnancy) during IVF treatment and offers an option for adjustments to be made during infertility treatment.

• Publisher: Sciendo
• Journal: Studies in Logic, Grammar and Rhetoric
• Year: 2015
• Volume: 43
• Issue: 1
• Pages: 7-20

Dates

published

2015-12-01

online

2016-01-06

Contributors

author

Anna Justyna Milewska

• Deartment of Statistics and Medical Informatics, Medical University of Bialystok, Poland

author

Dorota Jankowska

• Deartment of Statistics and Medical Informatics, Medical University of Bialystok, Poland

author

Urszula Cwalina

• Deartment of Statistics and Medical Informatics, Medical University of Bialystok, Poland

author

Dorota Citko

• Deartment of Statistics and Medical Informatics, Medical University of Bialystok, Poland

author

Teresa Więsak

• Department of Gamete and Embryo Biology, Institute of Animal Reproduction and Food Research of Polish Academy of Sciences, Olsztyn, Poland

author

Brian Acacio

• Acacio Fertility Center, Laguna Niguel, California, USA

author

Robert Milewski

• Deartment of Statistics and Medical Informatics, Medical University of Bialystok, Poland

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Identifiers

DOI

10.1515/slgr-2015-0038