Significance of Discriminant Analysis in Prediction of Pregnancy in IVF Treatment

Abstract

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.

Application of Linear Discriminant Function Analysis in Classification of Bovine Mastitis

“…91.1 percent of original grouped cases were correctly classified (predicted) by the discriminant model as seen in Table 3. Milewska et al, (2015) used discriminant model for predicting achievement and failure of pregnancy. They concluded that the discriminant analysis allowed for the creation of a model with a 51.22 percentage correctness of prediction to achieve pregnancy during in vitro fertilization treatment and with 74.07 percentage correctly predicted failure of pregnancy.…”