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...
Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
Published: |
Sciendo
2014-12-01
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Series: | Studies in Logic, Grammar and Rhetoric |
Online Access: | https://doi.org/10.2478/slgr-2014-0043 |
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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. |
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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 |
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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 |
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