急性呼吸道感染(ARIs)是全球5岁以下儿童死亡的主要原因。孕产妇寻求医疗保健的行为可能有助于将与ARI相关的死亡率降至最低,因为他们决定为子女提供医疗保健服务的种类和频率。因此,本研究旨在使用机器学习模型预测撒哈拉以南非洲(SSA)5岁以下儿童中缺乏孕产妇寻求医疗保健行为,并确定其相关因素.
■撒哈拉以南非洲国家的人口健康调查是数据集的来源。在这项研究中,我们使用了16832名五岁以下儿童的加权样本。使用Python(3.9版)处理数据,和机器学习模型,如极端梯度提升(XGB),随机森林,决策树,逻辑回归,并应用了朴素贝叶斯。在这项研究中,我们使用了评估指标,包括AUCROC曲线,准确度,精度,召回,和F测量,评估预测模型的性能。
■在这项研究中,在最终分析中使用了16,832名5岁以下儿童的加权样本.在提出的机器学习模型中,随机森林(RF)是预测最好的模型,准确率为88.89%,精度为89.5%,83%的F度量,AUCROC曲线为95.8%,77.6%的召回率预测母亲没有为ARIs寻求医疗保健的行为。与其他提出的模型相比,朴素贝叶斯的准确性最低(66.41%)。没有媒体曝光,生活在农村地区,不是母乳喂养,贫穷的财富地位,送货上门,没有ANC访问,没有母亲教育,35-49岁的母亲年龄组,与医疗机构的距离是母亲缺乏ARIs寻求医疗保健行为的重要预测因素。另一方面,营养不良的儿童,体重不足,浪费地位,腹泻,出生尺寸,已婚妇女,作为一个男性或女性的孩子,在5岁以下儿童中,有产妇职业与良好的产妇寻求ARIs的行为显著相关。
■RF模型提供了更大的预测能力,可以根据ARI风险因素估算母亲的寻求医疗保健行为。机器学习可以帮助高危ARI儿童实现早期预测和干预。这导致建议制定政策方向,以降低撒哈拉以南国家因ARI而导致的儿童死亡率。
UNASSIGNED: Acute respiratory infections (ARIs) are the leading cause of death in children under the age of 5 globally. Maternal healthcare-seeking behavior may help minimize mortality associated with ARIs since they make decisions about the kind and frequency of healthcare services for their children. Therefore, this study aimed to predict the absence of maternal healthcare-seeking behavior and identify its associated factors among children under the age 5 in sub-Saharan Africa (SSA) using machine learning models.
UNASSIGNED: The sub-Saharan African countries\' demographic health survey was the source of the dataset. We used a weighted sample of 16,832 under-five children in this study. The data were processed using Python (version 3.9), and machine learning models such as extreme gradient boosting (XGB), random forest, decision tree, logistic regression, and Naïve Bayes were applied. In this study, we used evaluation metrics, including the AUC ROC curve, accuracy, precision, recall, and F-measure, to assess the performance of the predictive models.
UNASSIGNED: In this study, a weighted sample of 16,832 under-five children was used in the final analysis. Among the proposed machine learning models, the random forest (RF) was the best-predicted model with an accuracy of 88.89%, a precision of 89.5%, an F-measure of 83%, an AUC ROC curve of 95.8%, and a recall of 77.6% in predicting the absence of mothers\' healthcare-seeking behavior for ARIs. The accuracy for Naïve Bayes was the lowest (66.41%) when compared to other proposed models. No media exposure, living in rural areas, not breastfeeding, poor wealth status, home delivery, no ANC visit, no maternal education, mothers\' age group of 35-49 years, and distance to health facilities were significant predictors for the absence of mothers\' healthcare-seeking behaviors for ARIs. On the other hand, undernourished children with stunting, underweight, and wasting status, diarrhea, birth size, married women, being a male or female sex child, and having a maternal occupation were significantly associated with good maternal healthcare-seeking behaviors for ARIs among under-five children.
UNASSIGNED: The RF model provides greater predictive power for estimating mothers\' healthcare-seeking behaviors based on ARI risk factors. Machine learning could help achieve early prediction and intervention in children with high-risk ARIs. This leads to a recommendation for policy direction to reduce child mortality due to ARIs in sub-Saharan countries.