The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome

Milewska, Anna Justyna; Jankowska, Dorota; Citko, Dorota; Więsak, Teresa; Acacio, Brian; Milewski, Robert

doi:10.2478/slgr-2014-0043

Cited by 18 publications

(14 citation statements)

References 38 publications

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“…The final analysis completed was a principal component analysis (PCA) that evaluates the trend among the variables. PCA is a data mining technique that reduces the redundancy among variables creating uncorrelated features called principal components with minimum information loss (Milewska et al 2014). PCA keeps as much variability in the data as possible, and enables visualization of observations (Milewska et al 2014).…”

Section: Resultsmentioning

confidence: 99%

“…PCA is a data mining technique that reduces the redundancy among variables creating uncorrelated features called principal components with minimum information loss (Milewska et al 2014). PCA keeps as much variability in the data as possible, and enables visualization of observations (Milewska et al 2014). When we evaluated the morphometry data, we found that the Percoll and Swim-Up groups had the same trend while the electro-channel group had a different trend (Figure 7).…”

Section: Resultsmentioning

confidence: 99%

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Morphometric analysis of sperm used for IVP by three different separation methods with spatial light interference microscopy

Rubessa

Kandel

Schreiber

et al. 2020

Systems Biology in Reproductive Medicine

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2020) Morphometric analysis of sperm used for IVP by three different separation methods with spatial light interference microscopy, Systems Biology in Reproductive Medicine, 66:1, 26-36, ABSTRACTThe goal of this study was to characterize sperm populations resulting from three different methods of sperm selection used for bovine in vitro fertilization. We compared sperm selection with discontinuous Percoll gradients, Swim-Up, and electro-channel. Spatial light interference microscopy (SLIM) was used to evaluate the morphology of the spermatozoa and computerassisted semen analysis (CASA) was used to evaluate the motility behavior of the sperm. Using these two technologies, we analyzed morphometric parameters and the kinetic (motility) patterns of frozen-thawed Holstein bull spermatozoa after sperm selection. For the first time, we have shown that these methods used to select viable spermatozoa for in vitro fertilization (IVF) result in very different sperm subpopulations. Almost every parameter evaluated resulted in statistical differences between treatment groups. One novel observation was that the dry mass of the sperm head is heavier in spermatozoa selected with the electro-channel than in sperm selected by the other methods. These results show the potential of SLIM microscopy in reproductive biology.Abbreviations: SLIM: spatial light interference microscopy; CASA: computer aided sperm analysis; IVF: in vitro fertilization; BSA: bovine serum albumin; QPI: quantitative phase imaging; IVEP: in vitro embryo production; IACUC: institutional animal care and use committee;

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Morphometric analysis of sperm used for IVP by three different separation methods with spatial light interference microscopy

Rubessa

Kandel

Schreiber

et al. 2020

Systems Biology in Reproductive Medicine

View full text Add to dashboard Cite

show abstract

“…They bring the same information as the primary variables and can be analysed using e.g. the logistic regression method [15]. On the other hand, the correlations between morphokinetic parameters are not a surprising phenomenon and indicate the existence of a certain cleavage pattern.…”

Section: Discussionmentioning

confidence: 99%

Morphokinetic parameters as a source of information concerning embryo developmental and implantation potential

et al. 2016

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Objectives: The aim of the study was to present the results of time-lapse observation and to verify whether morphokinetic parameters are associated with embryo developmental and implantation potential. Material and methods:The analysed data concern the development of 1,060 embryos, 898 of which (84.72%) achieved the blastocyst stage and 307 were transferred into the uterine cavity. As a result, 126 (41.04%) biochemical pregnancies and 109 (35.50%) clinical pregnancies were observed. Time from fertilisation to further divisions into 2-9 blastomeres, first to fourth round of cleavage, second to third synchronisation parameters and the duration of stages after the first, second and third division were analysed. Results:Most of the parameters in the group of embryos developed to the blastocyst stage reached lower values than in the non-developed group. Moreover, parameters in the first group clearly had less dispersion. The differences between the groups with and without a biochemical pregnancy were smaller than the differences in the analysis of development to the blastocyst stage. However, in the case of clinical pregnancy analysis, there were again larger differences between both groups. A strong correlation was found between the majority of absolute morphokinetic parameters. A weaker, but still statistically significant correlation, was established between relative and other parameters. Conclusions:Morphokinetic parameters are associated with embryo developmental and implantation potential and can be considered as predictors of their quality. However, the development of efficient pregnancy prediction models needs further research utilising information from all available parameters and using advanced biostatistical methods.

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“…Therefore, statistical models of differing levels of complexity that can handle multiple-size databases, such as data mining methods, are very useful . The most popular data mining methods are: the artificial neuron network (Milewski et al, 2009(Milewski et al, , 2013b, principal component analysis (Milewska et al, 2014), cluster analysis (Milewska et al, 2013), basket analysis and correspondence analysis (Milewska et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Significance of Discriminant Analysis in Prediction of Pregnancy in IVF Treatment

Milewska

Jankowska

Cwalina

et al. 2015

Studies in Logic, Grammar and Rhetoric

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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.

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The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome

Cited by 18 publications

References 38 publications

Morphometric analysis of sperm used for IVP by three different separation methods with spatial light interference microscopy

Morphometric analysis of sperm used for IVP by three different separation methods with spatial light interference microscopy

Morphokinetic parameters as a source of information concerning embryo developmental and implantation potential

Significance of Discriminant Analysis in Prediction of Pregnancy in IVF Treatment

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