Kuzu Adem, Santur Yunus
Department of Software Engineering, Firat University, Elazig 23119, Turkey.
Department of Artificial Intelligence and Data Engineering, Firat University, Elazig 23119, Turkey.
Diagnostics (Basel). 2023 Jul 25;13(15):2471. doi: 10.3390/diagnostics13152471.
(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.
(1) 背景：根据世界卫生组织（WHO）的数据，每年有630万例胎儿宫内死亡。诊断围产期死亡并对母婴健康采取早期预防措施的最常见方法是无应激试验（NST）。来自NST设备的胎儿心率和子宫收缩数据基于跟踪打印机的输出进行解读，以便专家做出胎儿健康诊断。(2) 方法：在本研究中，提出了一种基于集成学习的预测方法，用于使用NST测试中胎儿运动和胎儿心率加速的胎心监护数据集对胎儿健康（正常、可疑、病理）进行分类。(3) 结果：所提出的预测器在测试数据集上的准确率达到了99.5%以上。(4) 结论：从实验结果可以看出，使用机器学习可以在NST期间进行胎儿健康诊断。
