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

2014 
Principal Component Analysis is one of the data mining methods that can be used to analyze multidimensional datasets. The main objective of thismethodis a reductionof thenumberof studiedvariableswith themainte- nanceofasmuchinformation aspossible,uncoveringthestructureofthedata, its visualization as well as classification of the objects within the space defined bythenewlycreatedcomponents.PCAisveryoftenusedasapreliminarystep indatapreparationthroughthecreationofindependentcomponentsforfurther analysis. We used the PCA method as afirst step in analyzing data from IVF (in vitro fertilization). The next step and main purpose of the analysis was to createmodelsthatpredictpregnancy.Therefore, 805differenttypesofIVFcy- cleswereanalyzedandpregnancywascorrectlyclassifiedin61-80%ofcasesfor differentanalyzed groups inobtained models.
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