Early detection of fetal health status based on cardiotocography using artificial intelligence

Abstract

Abstract Fetal health is a vital aspect of pregnancy, influencing both the mother and her fetus. Frequent observation and prompt response are essential for achieving optimal outcomes. It is important to assess fetal health within the womb, ensuring that any potential issues are addressed rapidly. Prioritizing fetal monitoring is essential for safe and healthy pregnancy, one of such methods is Cardiotocography (CTG). CTG is employed to monitor the uterine contraction patterns and fetal heart rate during pregnancy and labor. The aim of this paper is to use artificial intelligence to enhance the accuracy of fetal health prediction and enhance clinical decision-making. Seven machine learning (ML) algorithms and five deep learning (DL) algorithms are applied. In addition, H 2 O.ai and Lazy predict platforms were applied for prediction. Ensemble learning was employed to combine the most effective models to construct the Blender model, emulate the traditional ML, DL models, and ML with DL in meta classifiers. The results for ML models showed that meta-model with stacking classifier had the highest accuracy of 98.9%. The results for DL models showed that ANN had the highest accuracy of 97.7%. The analysis of each model’s performance demonstrated that the proposed stacking classifier achieved 98.9% accuracy, 99% precision, 98.6% recall, 99.3% F1-score, and 99.8% area under the ROC curve. This implies that stacking classifier model demonstrates a strong capability in predicting fetal health and it can be integrated with the CTG device for real-time monitoring and medical follow-up by healthcare providers.

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