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 (Studies in Logic, Grammar and Rhetoric, 2014)
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.