Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models

dc.contributor.author	Comert, Zafer
dc.contributor.author	Sengur, Abdulkadir
dc.contributor.author	Budak, Umit
dc.contributor.author	Kocamaz, Adnan Fatih
dc.date.accessioned	2021-12-16T09:06:56Z
dc.date.available	2021-12-16T09:06:56Z
dc.date.issued	2019
dc.identifier.issn	2047-2501
dc.identifier.uri	https://doi.org/10.1007/s13755-019-0079-z
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/10082
dc.description.abstract	Introduction Cardiotocography (CTG) consists of two biophysical signals that are fetal heart rate (FHR) and uterine contraction (UC). In this research area, the computerized systems are usually utilized to provide more objective and repeatable results. Ma
dc.language.iso	English
dc.publisher	Sprınger
dc.rights	Green Published
dc.source	Health Informatıon Scıence And Systems
dc.title	Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models
dc.type	Article
dc.identifier.issue	1
dc.identifier.doi	10.1007/s13755-019-0079-z
dc.identifier.wos	WOS:000482780300001
dc.identifier.volume	7

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