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

Principal Component Analysis is one of the data mining methods that can be used to analyze multidimensional datasets. The main objective of this method is a reduction of the number of studied variables with the mainte- nance of as much information as possible, uncovering the structure of the data, i...

Full description

Bibliographic Details
Main Authors: Milewska Anna Justyna, Jankowska Dorota, Citko Dorota, Więsak Teresa, Acacio Brian, Milewski Robert
Format: Article
Language:English
Published: Sciendo 2014-12-01
Series:Studies in Logic, Grammar and Rhetoric
Online Access:https://doi.org/10.2478/slgr-2014-0043
_version_ 1818730428059615232
author Milewska Anna Justyna
Jankowska Dorota
Citko Dorota
Więsak Teresa
Acacio Brian
Milewski Robert
author_facet Milewska Anna Justyna
Jankowska Dorota
Citko Dorota
Więsak Teresa
Acacio Brian
Milewski Robert
author_sort Milewska Anna Justyna
collection DOAJ
description Principal Component Analysis is one of the data mining methods that can be used to analyze multidimensional datasets. The main objective of this method is a reduction of the number of studied variables with the mainte- nance of as much information as possible, uncovering the structure of the data, its visualization as well as classification of the objects within the space defined by the newly created components. PCA is very often used as a preliminary step in data preparation through the creation of independent components for further analysis. We used the PCA method as a first step in analyzing data from IVF (in vitro fertilization). The next step and main purpose of the analysis was to create models that predict pregnancy. Therefore, 805 different types of IVF cy- cles were analyzed and pregnancy was correctly classified in 61-80% of cases for different analyzed groups in obtained models.
first_indexed 2024-12-17T23:01:37Z
format Article
id doaj.art-a8e6e868b54846a19faef1356ccb72e2
institution Directory Open Access Journal
issn 0860-150X
2199-6059
language English
last_indexed 2024-12-17T23:01:37Z
publishDate 2014-12-01
publisher Sciendo
record_format Article
series Studies in Logic, Grammar and Rhetoric
spelling doaj.art-a8e6e868b54846a19faef1356ccb72e22022-12-21T21:29:23ZengSciendoStudies in Logic, Grammar and Rhetoric0860-150X2199-60592014-12-0139172310.2478/slgr-2014-0043slgr-2014-0043The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment OutcomeMilewska Anna Justyna0Jankowska Dorota1Citko Dorota2Więsak Teresa3Acacio Brian4Milewski Robert5Department of Statistics and Medical Informatics, Medical University of Bialystok, PolandDepartment of Statistics and Medical Informatics, Medical University of Bialystok, PolandDepartment of Statistics and Medical Informatics, Medical University of Bialystok, PolandDepartment of Gamete and Embryo Biology, Institute of Animal Reproduction and Food Research of Polish Academy of Sciences in Olsztyn, PolandAcacio Fertility Center, Laguna Niguel, California, USADepartment of Statistics and Medical Informatics, Medical University of Bialystok, PolandPrincipal Component Analysis is one of the data mining methods that can be used to analyze multidimensional datasets. The main objective of this method is a reduction of the number of studied variables with the mainte- nance of as much information as possible, uncovering the structure of the data, its visualization as well as classification of the objects within the space defined by the newly created components. PCA is very often used as a preliminary step in data preparation through the creation of independent components for further analysis. We used the PCA method as a first step in analyzing data from IVF (in vitro fertilization). The next step and main purpose of the analysis was to create models that predict pregnancy. Therefore, 805 different types of IVF cy- cles were analyzed and pregnancy was correctly classified in 61-80% of cases for different analyzed groups in obtained models.https://doi.org/10.2478/slgr-2014-0043
spellingShingle Milewska Anna Justyna
Jankowska Dorota
Citko Dorota
Więsak Teresa
Acacio Brian
Milewski Robert
The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome
Studies in Logic, Grammar and Rhetoric
title The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome
title_full The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome
title_fullStr The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome
title_full_unstemmed The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome
title_short The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome
title_sort use of principal component analysis and logistic regression in prediction of infertility treatment outcome
url https://doi.org/10.2478/slgr-2014-0043
work_keys_str_mv AT milewskaannajustyna theuseofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome
AT jankowskadorota theuseofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome
AT citkodorota theuseofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome
AT wiesakteresa theuseofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome
AT acaciobrian theuseofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome
AT milewskirobert theuseofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome
AT milewskaannajustyna useofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome
AT jankowskadorota useofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome
AT citkodorota useofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome
AT wiesakteresa useofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome
AT acaciobrian useofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome
AT milewskirobert useofprincipalcomponentanalysisandlogisticregressioninpredictionofinfertilitytreatmentoutcome