Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them

Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them

Abstract Background This study sought to provide machine learning-based classification models to predict the success of intrauterine insemination (IUI) therapy. Additionally, we sought to illustrate the effect of models fitting with balanced data vs original data with imbalanced data labels using tw...

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Bibliographic Details
Main Authors: Sajad Khodabandelu, Zahra Basirat, Sara Khaleghi, Soraya Khafri, Hussain Montazery Kordy, Masoumeh Golsorkhtabaramiri
Format: Article
Language:English
Published: BMC 2022-09-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Machine learning
Imbalanced data
Intrauterine insemination
Infertility
Cumulative live birth
Online Access:https://doi.org/10.1186/s12911-022-01974-8

Internet

https://doi.org/10.1186/s12911-022-01974-8

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