Effective techniques for intelligent cardiotocography interpretation using XGB-RF feature selection and stacking fusion.

Cardiotocography (CTG) monitoring is a primary tool to assess the health of the fetus. It is widely used to identify the risk of fetal distress. With the outbreak of big data and artificial intelligence, the use of machine learning to assist obstetricians in CTG interpretation is important to improve diagnostic accuracy and save medical resources. However, imbalanced CTG data brings great challenges to machine learning in intelligent multi-classification. Therefore, we propose a stacked model based on Extreme-Gradient Boosting and Random Forest (XGB-RF) feature selection. Firstly, we use the XGB-RF feature selection method to extract the CTG features of significant influence. Then, we implement the fusion idea of stacking to construct the intelligent model that has a strong anti-interference ability under unbalanced CTG data with selected diverse classifiers. The experimental results showed that compared with existing CTG classification models in the public CTG dataset, the proposed model has further improved the performance and reduced the misjudgment between different classes. According to the results, the accuracy is 96.08%, the F1 score is 93.36%, and area under the ROC curve (AUC) is 0.9883. This indicates that the proposed techniques have a great clinical significance in fetal health monitoring.