A METHOD FOR EARLY PREDICTION OF IVF OUTCOMES IN WOMEN WITH ANOVULATORY INFERTILITY BASED ON GENETIC AND HORMONAL STATUS USING ARTIFICIAL INTELLIGENCE
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Abstract
The article discusses the developed the IVFClassifier software module for early prediction of IVF outcomes in women with anovulatory infertility based on genetic markers and hormonal status.
A database was formed including polymorphisms of the KISS1, FSHR, MTNR1B, and LEPR genes together with hormonal-profile parameters. The predictive model was built using a support vector machine with an RBF kernel and logistic regression. Nested cross-validation, automated hyperparameter optimization, and fold-based model ensembling were applied to improve reliability. A graphical user interface was implemented to enter the initial data and obtain a prediction of IVF effectiveness, the presumed genotype, and a confidence indicator. The developed software package can be used as a decision-support tool for personalized assessment of IVF prospects.
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The authors declare that they have no apparent or potential conflicts of interest related to the publication of this article.
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