Mamo Daniel Niguse, Walle Agmasie Damtew, Woldekidan Eden Ketema, Adem Jibril Bashir, Gebremariam Yosef Haile, Alemayehu Meron Asmamaw, Enyew Ermias Bekele, Kebede Shimels Derso
Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Arbaminch University, Arbaminch, Ethiopia.
Department of Health Informatics, School of Public Health, Asrat Woldeyes Health Science Campus, Debre Berhan University, Debre Birhan, Ethiopia.
PLOS Digit Health. 2025 Jan 9;4(1):e0000707. doi: 10.1371/journal.pdig.0000707. eCollection 2025 Jan.
Postnatal care refers to the support provided to mothers and their newborns immediately after childbirth and during the first six weeks of life, a period when most maternal and neonatal deaths occur. In the 30 countries studied, nearly 40 percent of women did not receive a postpartum care check-up. This research aims to evaluate and compare the effectiveness of machine learning algorithms in predicting postnatal care utilization in Ethiopia and to identify the key factors involved. The study employs machine learning techniques to analyse secondary data from the 2016 Ethiopian Demographic and Health Survey. It aims to predict postnatal care utilization and identify key predictors via Python software, applying fifteen machine-learning algorithms to a sample of 7,193 women. Feature importance techniques were used to select the top predictors. The models' effectiveness was evaluated using sensitivity, specificity, F1 score, precision, accuracy, and area under the curve. Among the four experiments, tenfold cross-validation with balancing using Synthetic Minority Over-sampling Technique was outperformed. From fifteen models, the MLP Classifier (f1 score = 0.9548, AUC = 0.99), Random Forest Classifier (f1 score = 0.9543, AUC = 0.98), and Bagging Classifier (f1 score = 0.9498, AUC = 0.98) performed excellently, with a strong ability to differentiate between classes. The Region, residence, maternal education, religion, wealth index, health insurance status, and place of delivery are identified as contributing factors that predict postnatal care utilization. This study assessed machine learning models for forecasting postnatal care usage. Ten-fold cross-validation with Synthetic Minority Oversampling Technique produced the best results, emphasizing the significance of addressing class imbalance in healthcare datasets. This approach enhances the accuracy and dependability of predictive models. Key findings reveal regional and socioeconomic factors influencing PNC utilization, which can guide targeted initiatives to improve postnatal care utilization and ultimately enhance maternal and child health.
产后护理是指在分娩后及新生儿生命的头六周内为母亲及其新生儿提供的支持，这是大多数孕产妇和新生儿死亡发生的时期。在所研究的30个国家中，近40%的妇女没有接受产后护理检查。本研究旨在评估和比较机器学习算法在预测埃塞俄比亚产后护理利用情况方面的有效性，并确定其中涉及的关键因素。该研究采用机器学习技术分析2016年埃塞俄比亚人口与健康调查的二手数据。其目的是通过Python软件预测产后护理利用情况，并识别关键预测因素，将15种机器学习算法应用于7193名妇女的样本。使用特征重要性技术来选择顶级预测因素。使用灵敏度、特异性、F1分数、精确度、准确度和曲线下面积来评估模型的有效性。在四个实验中，使用合成少数过采样技术进行平衡的十折交叉验证表现更佳。在15个模型中，多层感知器分类器（F1分数 = 0.9548，AUC = 0.99）、随机森林分类器（F1分数 = 0.9543，AUC = 0.98）和装袋分类器（F1分数 = 0.9498，AUC = 0.98）表现出色，具有很强的区分不同类别的能力。地区、居住地、母亲教育程度、宗教、财富指数、健康保险状况和分娩地点被确定为预测产后护理利用情况的影响因素。本研究评估了用于预测产后护理使用情况的机器学习模型。使用合成少数过采样技术的十折交叉验证产生了最佳结果，强调了在医疗保健数据集中解决类别不平衡问题的重要性。这种方法提高了预测模型的准确性和可靠性。主要研究结果揭示了影响产后护理利用情况的地区和社会经济因素，这可以指导有针对性的举措来提高产后护理利用率，并最终改善母婴健康。
