Yehuala Tirualem Zeleke, Fente Bezawit Melak, Wubante Sisay Maru
Department Health informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
Department of General Midwifery, School of Midwifery, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
BMC Pregnancy Childbirth. 2025 May 9;25(1):551. doi: 10.1186/s12884-025-07668-z.
The occurrence of short birth intervals among reproductive-age women in East Africa is a critical public health issue, contributing to maternal and child health risks. Identifying the key factors that predict short birth intervals can help design targeted interventions to reduce these risks. Hence, this study aimed to predict short birth intervals and identify their determinants using supervised machine learning models.
This study employs machine learning algorithms to predict short birth intervals among reproductive-age women in East Africa, using a dataset from Demographic and Health Surveys. The dataset undergoes preprocessing steps to handle missing values, encode categorical variables, perform feature selection, and integrate data and normalize numerical features. Four machine learning models, including logistic regression, decision trees, random forests, and some machine learning models, including logistic regression, decision trees, random forests, and naive Bayes, are trained and evaluated to predict short birth intervals. Model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC used to ensure reliable results.
The machine learning models identified several key factors that significantly predict short birth intervals among reproductive-age women in East Africa. The Random Forest models demonstrated the highest accuracy (79.4%), precision (79.0%), F-score (84.0%), ROC curve (83.8%), and recall (91.0%), with feature importance analysis highlighting maternal age, educational status, parity, use of family planning, and access to healthcare as the most influential predictors. The findings underscore the importance of targeted interventions addressing healthcare access and family planning to reduce the risks associated with short birth intervals in East African countries.
The study demonstrates that machine learning models can effectively identify key predictors of short birth intervals among reproductive-age women in East Africa, providing valuable insights for designing targeted public health interventions to improve maternal and child health outcomes in East Africa.
东非育龄妇女中短生育间隔的出现是一个关键的公共卫生问题，会增加母婴健康风险。确定预测短生育间隔的关键因素有助于设计有针对性的干预措施以降低这些风险。因此，本研究旨在使用监督机器学习模型预测短生育间隔并确定其决定因素。
本研究采用机器学习算法，利用人口与健康调查数据集预测东非育龄妇女的短生育间隔。该数据集经过预处理步骤，以处理缺失值、编码分类变量、进行特征选择以及整合数据并对数值特征进行归一化。训练并评估了四种机器学习模型，包括逻辑回归、决策树、随机森林以及朴素贝叶斯，以预测短生育间隔。使用准确率、精确率、召回率、F1分数和AUC-ROC等指标评估模型性能，以确保结果可靠。
机器学习模型确定了几个显著预测东非育龄妇女短生育间隔的关键因素。随机森林模型表现出最高的准确率（79.4%）、精确率（79.0%）、F分数（84.0%）、ROC曲线（83.8%）和召回率（91.0%），特征重要性分析突出显示母亲年龄、教育程度、产次、计划生育的使用情况以及获得医疗保健的机会是最具影响力的预测因素。研究结果强调了针对性干预措施在解决医疗保健可及性和计划生育方面的重要性，以降低东非国家与短生育间隔相关的风险。
该研究表明，机器学习模型可以有效地识别东非育龄妇女短生育间隔的关键预测因素，为设计有针对性的公共卫生干预措施以改善东非的母婴健康结果提供有价值的见解。
