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Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning.

Authors :
Kuzu, Adem
Santur, Yunus
Source :
Diagnostics (2075-4418). Aug2023, Vol. 13 Issue 15, p2471. 15p.
Publication Year :
2023

Abstract

(1) Background: According to the World Health Organization (WHO), 6.3 million intrauterine fetal deaths occur every year. The most common method of diagnosing perinatal death and taking early precautions for maternal and fetal health is a nonstress test (NST). Data on the fetal heart rate and uterus contractions from an NST device are interpreted based on a trace printer's output, allowing for a diagnosis of fetal health to be made by an expert. (2) Methods: in this study, a predictive method based on ensemble learning is proposed for the classification of fetal health (normal, suspicious, pathology) using a cardiotocography dataset of fetal movements and fetal heart rate acceleration from NST tests. (3) Results: the proposed predictor achieved an accuracy level above 99.5% on the test dataset. (4) Conclusions: from the experimental results, it was observed that a fetal health diagnosis can be made during NST using machine learning. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20754418
Volume :
13
Issue :
15
Database :
Academic Search Index
Journal :
Diagnostics (2075-4418)
Publication Type :
Academic Journal
Accession number :
169910764
Full Text :
https://doi.org/10.3390/diagnostics13152471