Owusu-Adjei Michael, Ben Hayfron-Acquah James, Frimpong Twum, Gaddafi Abdul-Salaam
Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
PLOS Digit Health. 2025 Feb 5;4(2):e0000543. doi: 10.1371/journal.pdig.0000543. eCollection 2025 Feb.
The desire for safer delivery mode that preserves the lives of both mother and child with minimal or no complications before, during and after childbirth is the wish for every expectant mother and their families. However, the choice for any particular delivery mode is supposedly influenced by a number of factors that leads to the ultimate decision of choice. Some of the factors identified include maternal birth history, maternal and child health conditions prevailing before and during labor onset. Predictive modeling has been used extensively to determine important contributory factors or artifacts influencing delivery choice in related research studies. However, missing among a myriad of features used in various research studies for this determination is maternal history of spontaneous, threatened and inevitable abortion(s). How its inclusion impacts delivery outcome has not been covered in extensive research work. This research work therefore takes measurable maternal features that include real time information on administered partographs to predict delivery outcome. This is achieved by adopting effective feature selection technique to estimate variable relationships with the target variable. Three supervised learning techniques are used and evaluated for performance. Prediction accuracy score of area under the curve obtained show Gradient Boosting classifier achieved 91% accuracy, Logistic Regression 93% and Random Forest 91%. Balanced accuracy score obtained for these techniques were; Gradient Boosting 82.73%, Logistic Regression 84.62% and Random Forest 83.02%. Correlation statistic for variable independence among input variables showed that delivery outcome type as an output is associated with fetal gestational age and the progress of maternal cervix dilatation during labor onset.
在分娩前、分娩期间和分娩后，希望有一种更安全的分娩方式，能以最小的并发症或无并发症的方式保护母婴的生命，这是每一位准妈妈及其家人的愿望。然而，任何特定分娩方式的选择可能会受到一些因素的影响，这些因素会导致最终的选择决定。已确定的一些因素包括产妇的分娩史、分娩开始前和期间的母婴健康状况。在相关研究中，预测模型已被广泛用于确定影响分娩选择的重要促成因素或因素。然而，在各种研究中用于此确定的众多特征中，缺少产妇自然流产、先兆流产和难免流产的病史。其纳入如何影响分娩结果在广泛的研究工作中尚未涉及。因此，这项研究工作采用可测量的产妇特征，包括关于使用产程图的实时信息来预测分娩结果。这是通过采用有效的特征选择技术来估计变量与目标变量之间的关系来实现的。使用并评估了三种监督学习技术的性能。获得的曲线下面积预测准确率得分显示，梯度提升分类器达到91%的准确率，逻辑回归为93%，随机森林为91%。这些技术获得的平衡准确率得分分别为：梯度提升82.73%，逻辑回归84.62%，随机森林83.02%。输入变量之间变量独立性的相关统计表明，作为输出的分娩结果类型与胎儿孕周和分娩开始时产妇宫颈扩张的进展有关。
