Open AccessMachine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies
Author(s) -
Tae Jun Park,
Hye Jin Chang,
Bunsoon Choi,
Jung Ah Jung,
Seongwoo Kang,
Seokyoung Yoon,
Miran Kim,
Dukyong Yoon
Publication year - 2022
Publication title -
yonsei medical journal/yonsei medical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.702
H-Index - 63
eISSN - 1976-2437
pISSN - 0513-5796
DOI - 10.3349/ymj.2022.63.7.692
Subject(s) - cardiotocography , odds ratio , medicine , diagnostic odds ratio , receiver operating characteristic , apgar score , fetal distress , odds , obstetrics , intraclass correlation , fetus , logistic regression , pregnancy , clinical psychology , genetics , biology , psychometrics
Fetal well-being is usually assessed via fetal heart rate (FHR) monitoring during the antepartum period. However, the interpretation of FHR is a complex and subjective process with low reliability. This study developed a machine learning model that can classify fetal cardiotocography results as normal or abnormal.