Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning.
Diagnostics (Basel)
; 13(15)2023 Jul 25.
Article
en En
| MEDLINE
| ID: mdl-37568833
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Screening_studies
Aspecto:
Patient_preference
Idioma:
En
Revista:
Diagnostics (Basel)
Año:
2023
Tipo del documento:
Article
País de afiliación:
Turquía
Pais de publicación:
CH
/
SUIZA
/
SUÍÇA
/
SWITZERLAND