Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population.
J Assist Reprod Genet
; 41(8): 2173-2183, 2024 Aug.
Article
em En
| MEDLINE
| ID: mdl-38819714
ABSTRACT
PURPOSE:
This study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese population.METHODS:
RESULTS:
A total of 11 variables, including eight from female (age, body mass index, duration of infertility, prior miscarriage, and spontaneous abortion), hormone levels (anti-Müllerian hormone, follicle-stimulating hormone, luteinizing hormone), and three from male (smoking, semen volume, and sperm concentration), were identified as the significant variables associated with IUI clinical pregnancy in our Chinese dataset. The RF-based prediction model presents an area under the receiver operating characteristic curve (AUC) of 0.716 (95% confidence interval, 0.6914-0.7406), an accuracy rate of 0.6081, a sensitivity rate of 0.7113, and a specificity rate of 0.505. Importance analysis indicated that semen volume was the most vital variable in predicting IUI clinical pregnancy.CONCLUSIONS:
The machine learning-based IUI clinical pregnancy prediction model showed a promising predictive efficacy that could provide a potent tool to guide selecting targeted infertile couples beneficial from IUI treatment, and also identify which parameters are most relevant in IUI clinical pregnancy.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Inseminação Artificial
/
Aprendizado de Máquina
Limite:
Adult
/
Female
/
Humans
/
Male
/
Pregnancy
País/Região como assunto:
Asia
Idioma:
En
Revista:
J Assist Reprod Genet
Assunto da revista:
GENETICA
/
MEDICINA REPRODUTIVA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China