Key result
A random forest algorithm was suggested and tested for the automatic recognition of physiological, suspect, and pathological fetal states from cardiotocography records.
Why the study?
Can a random forest algorithm automatically recognize physiological, suspect, and pathological fetal states from cardiotocography records?
Can a random forest algorithm automatically recognize physiological, suspect, and pathological fetal states from cardiotocography records?
The study proposes a random forest algorithm for the automatic classification of fetal states from cardiotocography records to support clinical decision-making in prenatal care.
May assist automated CTG interpretation; leaves open need for prospective validation before clinical use.
The cardiotocography (CTG) is a diagnostic method which is widely used in prenatal care. The CTG is indicated since 27 weeks of pregnancy and it measures heart activity, uterine contraction and fetal movement. Results of the CTG allow recognizing of three basic different fetal states (physiological, suspect and pathological) and an obstetrician can determine a diagnosis and evaluate situation which can lead to the fetus death. The main aim of this work is to suggest and to test algorithm for automatic recognition of above mentioned states. This task is especially used in prenatal care as a support decision system.
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Peterek et al. (2013) studied Fetal state monitoring in prenatal care. Random forest algorithm was evaluated on Automatic recognition of fetal states (physiological, suspect, and pathological). A random forest algorithm was suggested and tested for the automatic recognition of physiological, suspect, and pathological fetal states from cardiotocography records.
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