ArticleFrontiers in cell and developmental biology2022
Classification of Cardiotocography Based on the Apriori Algorithm and Multi-Model Ensemble Classifier.
Article in Frontiers in cell and developmental biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Characterization of the stress level of university students using data mining algorithms.Frontiers in research metrics and analytics · 2025Article
- The use of machine and deep learning to model the relationship between discomfort temperature and labor productivity loss among petrochemical workers.BMC public health · 2024Article
- Prediction of miRNA-Disease Associations by Cascade Forest Model Based on Stacked Autoencoder.Molecules (Basel, Switzerland) · 2023Article
- A Fetal Well-Being Diagnostic Method Based on Cardiotocographic Morphological Pattern Utilizing Autoencoder and Recursive Feature Elimination.Diagnostics (Basel, Switzerland) · 2023Article
- Machine learning and disease prediction in obstetrics.Current research in physiology · 2023Review
- LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions.Frontiers in microbiology · 2022Article
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Abstract
Cardiotocography (CTG) recorded fetal heart rate and its temporal relationship with uterine contractions. CTG intelligent classification plays an important role in evaluating fetal health and protecting fetal normal growth and development throughout pregnancy. At the feature selection level, this study uses the Apriori algorithm to search frequent item sets for feature extraction. At the level of the classification model, the combination model of AdaBoost and random forest with the highest classification accuracy is finally selected by comparing various models. The suspicious class data in the CTG data set affect the overall classification accuracy. The number of suspicious class data is predicted by the multi-model ensemble method. Finally, the data set is fused from three classifications to two classifications. The classification accuracy is 0.976, and the AUC is 0.98, which significantly improves the classification effect. In conclusion, the method used in this study has high accuracy in model classification, which is helpful to improve the accuracy of fetal abnormality detection.
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