Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania

Autor:	Clifford Silver Tarimo, Soumitra S. Bhuyan, Yizhen Zhao, Weicun Ren, Akram Mohammed, Quanman Li, Marilyn Gardner, Michael Johnson Mahande, Yuhui Wang, Jian Wu
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Low five-minute Apgar score Successful labor induction Machine learning Imbalanced data North-Tanzania Gynecology and obstetrics RG1-991
Zdroj:	BMC Pregnancy and Childbirth, Vol 22, Iss 1, Pp 1-14 (2022)
Druh dokumentu:	article
ISSN:	1471-2393
DOI:	10.1186/s12884-022-04534-0
Popis:	Abstract Background Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes. We set out to investigate important attributes and train popular machine learning (ML) algorithms to correctly classify neonates with a low Apgar scores from an imbalanced learning perspective. Methods We analyzed 7716 induced vaginal deliveries from the electronic birth registry of the Kilimanjaro Christian Medical Centre (KCMC). 733 (9.5%) of which constituted of low (
Databáze:	Directory of Open Access Journals
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