ArticleDiagnostics (Basel, Switzerland)2023
Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Enhance autism spectrum disorder detection using stacking ensemble learning model with explainable AI.BioData mining · 2026Article
- Clinical utility assessment framework for machine learning-based fetal health classification in cardiotocography: an observational study.Obstetrics & gynecology science · 2026Article
- A multi-modal AI framework integrating Siamese networks and few-shot learning for early fetal health risk assessment.MethodsX · 2025Article
- Fetal Health Diagnosis Based on Adaptive Dynamic Weighting with Main-Auxiliary Correction Network.Biotech (Basel (Switzerland)) · 2025Article
- Early Detection of Fetal Health Conditions Using Machine Learning for Classifying Imbalanced Cardiotocographic Data.Diagnostics (Basel, Switzerland) · 2025Article
- A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization.Interdisciplinary sciences, computational life sciences · 2024Article
- BrainNet: an automated approach for brain stress prediction utilizing electrodermal activity signal with XLNet model.Frontiers in computational neuroscience · 2024Article
- Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI.Frontiers in public health · 2024Article
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2 authors.
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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.
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