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

Khodabandelu, Sajad; Basirat, Zahra; Khaleghi, Sara; Khafri, Soraya; Montazery Kordy, Hussain; Golsorkhtabaramiri, Masoumeh

Khodabandelu, Sajad; Basirat, Zahra; Khaleghi, Sara; Khafri, Soraya; Montazery Kordy, Hussain; Golsorkhtabaramiri, Masoumeh.

Afiliación

Khodabandelu S; Student Research Committee, Babol University of Medical Sciences, Babol, Iran.
Basirat Z; Infertility and Reproductive Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran.
Khaleghi S; Student Research Committee, Babol University of Medical Sciences, Babol, Iran.
Khafri S; Infertility and Reproductive Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran. khafri@yahoo.com.
Montazery Kordy H; Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.
Golsorkhtabaramiri M; Infertility and Reproductive Health Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran.

BMC Med Inform Decis Mak ; 22(1): 228, 2022 09 01.

Article en En | MEDLINE | ID: mdl-36050710

Asunto(s)

Semen; Motilidad Espermática; Adulto; Estudios Transversales; Femenino; Humanos; Inseminación; Inseminación Artificial; Aprendizaje Automático; Masculino; Embarazo; Adulto Joven

Palabras clave

Cumulative live birth; Imbalanced data; Infertility; Intrauterine insemination; Machine learning

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Semen / Motilidad Espermática Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Female / Humans / Male / Pregnancy Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Irán

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