Kelkay Jenberu Mekurianew, Anteneh Deje Sendek, Wubneh Henok Dessie, Gessesse Abraham Dessie, Gebeyehu Gebeyehu Fassil, Aweke Kalkidan Kassahun, Ejigu Mikiyas Birhanu, Sendeku Mathias Amare, Barkneh Kirubel Adrissie, Demissie Hasset Girma, Negash Wubshet D, Mihret Birku Getie
Department of Health Informatics, College of Health Sciences, Debark University, Debark, Ethiopia.
Department of Health Informatics, Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia.
BMC Pregnancy Childbirth. 2025 Feb 5;25(1):121. doi: 10.1186/s12884-025-07248-1.
A birth interval of less than 33 months was considered short, and in low- income countries like Ethiopia, a short birth interval is the primary cause of approximately 822 maternal deaths every day. Due to that this study aimed to predict short birth interval and associated factors among women (15-49) in Ethiopia using ensemble learning algorithms.
A secondary data analysis of Ethiopian demographic health servey from 2016 to 2019 was performed. a total of weighted sample of 12,573 women in the reproductive age group was included in this study. Data have been extracted and processed with Stata version 17. The dataset was then imported into a Jupyter notebook for further detailed analysis and visualization. An ensemble Machin learning algorithm using different classification models were implemented. All analysis and calculation were performed using Python 3 programming language in Jupyter Notebook using imblearn, sklearn, and xgboost pakages.
Random forest demonstrated the best performance with an accuracy 97.84%, recall of 99.70%, F1-score of 97.81%, 98.95% precision on test data and AUC (98%). Region, residency, age of women, sex of child, respondent education, distance health facility, husband education and religion were top predicting factors of short birth interval among women in Ethiopia.
Random forest was best predictive models with improved performance. "The most significant features that contribute to the accuracy of the top-performing models, notably the Random Forest should be highlighted because they outperformed the other model in the analysis.In general, ensemble learning algorithms can accurately predict short birth interval status, making them potentially useful as decision-support tools for the pertinent stakeholders.
分娩间隔小于33个月被视为短间隔，在埃塞俄比亚等低收入国家，短分娩间隔是每天约822例孕产妇死亡的主要原因。因此，本研究旨在使用集成学习算法预测埃塞俄比亚15至49岁女性的短分娩间隔及相关因素。
对2016年至2019年埃塞俄比亚人口与健康调查进行二次数据分析。本研究纳入了12573名育龄妇女的加权样本。数据已使用Stata 17版本进行提取和处理。然后将数据集导入Jupyter笔记本进行进一步详细分析和可视化。实施了使用不同分类模型的集成机器学习算法。所有分析和计算均使用Python 3编程语言在Jupyter笔记本中使用imblearn、sklearn和xgboost包进行。
随机森林表现最佳，在测试数据上的准确率为97.84%，召回率为99.70%，F1分数为97.81%，精确率为98.95%，曲线下面积为98%。地区、居住情况、妇女年龄、孩子性别、受访者教育程度、到医疗机构的距离、丈夫教育程度和宗教信仰是埃塞俄比亚女性短分娩间隔的主要预测因素。
随机森林是性能最佳的预测模型。“应突出对表现最佳的模型（尤其是随机森林）的准确性有贡献的最重要特征，因为它们在分析中优于其他模型。总体而言，集成学习算法可以准确预测短分娩间隔状态，使其有可能作为相关利益攸关方的决策支持工具。
