Gynecology & Obstetrics
Open Access

Rinehart John

Publications

• Case Report
  An Application of Machine Learning in IVF: Comparing the Accuracy of Classification Alogithims for the Prediction of Twins
  Author(s): Rinehart JohnRinehart John
  
  Background: Clinical decision-making dilemmas are particularly notable in IVF practice, given that large datasets are often generated which enable clinicians to make predictions that inform treatment choices. This study applied machine learning by using IVF data to determine the risk of twins when two or more embryos are available for transfer. While most classifiers are able to provide estimates of accuracy, this study went further by comparing classifiers both by accuracy and Area Under the Curve (AUC).Methods: Study data were derived from a large electronic medical record system that is utilized by over 140 IVF clinics and contained 135,000 IVF cycles. The dataset was reduced from 88 variables to 40 and included only those cycles of IVF where two or more blastocyst embryos were created. The following classifiers were compared in terms of accu.. View More»
  DOI: 10.4172/2161-0932.1000497

  Abstract PDF

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