Yehuala Tirualem Zeleke, Fente Bezawit Melak, Wubante Sisay Maru, Derseh Nebiyu Mekonnen
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.
Front Pediatr. 2024 Nov 20;12:1388820. doi: 10.3389/fped.2024.1388820. eCollection 2024.
The primary cause of death for children under the age of five is acute respiratory infections (ARI). Early predicting acute respiratory tract infections (ARI) and identifying their predictors using supervised machine learning algorithms is the most effective way to save the lives of millions of children. Hence, this study aimed to predict acute respiratory tract infections (ARI) and identify their determinants using the current state-of-the-art machine learning models.
We used the most recent demographic and health survey (DHS) dataset from 36 Sub-Saharan African countries collected between 2005 and 2022. Python software was used for data processing and machine learning model building. We employed five machine learning algorithms, such as Random Forest, Decision Tree (DT), XGBoost, Logistic Regression (LR), and Naive Bayes, to analyze risk factors associated with ARI and predict ARI in children. We evaluated the predictive models' performance using performance assessment criteria such as accuracy, precision, recall, and the AUC curve.
In this study, 75,827 children under five were used in the final analysis. Among the proposed machine learning models, random forest performed best overall in the proposed classifier, with an accuracy of 96.40%, precision of 87.9%, F-measure of 82.8%, ROC curve of 94%, and recall of 78%. Naïve Bayes accuracy has also achieved the least classification with accuracy (87.53%), precision (67%), F-score (48%), ROC curve (82%), and recall (53%). The most significant determinants of preventing acute respiratory tract infection among under five children were having been breastfed, having ever been vaccinated, having media exposure, having no diarrhea in the last two weeks, and giving birth in a health facility. These were associated positively with the outcome variable.
According to this study, children who didn't take vaccinations had weakened immune systems and were highly affected by ARIs in Sub-Saharan Africa. The random forest machine learning model provides greater predictive power for estimating acute respiratory infections and identifying risk factors. This leads to a recommendation for policy direction to reduce infant mortality in Sub-Saharan Africa.
五岁以下儿童的主要死因是急性呼吸道感染（ARI）。使用监督式机器学习算法早期预测急性呼吸道感染（ARI）并识别其预测因素是拯救数百万儿童生命的最有效方法。因此，本研究旨在使用当前最先进的机器学习模型预测急性呼吸道感染（ARI）并识别其决定因素。
我们使用了2005年至2022年期间从36个撒哈拉以南非洲国家收集的最新人口与健康调查（DHS）数据集。使用Python软件进行数据处理和机器学习模型构建。我们采用了五种机器学习算法，如随机森林、决策树（DT）、XGBoost、逻辑回归（LR）和朴素贝叶斯，来分析与ARI相关的风险因素并预测儿童的ARI。我们使用准确性、精确性、召回率和AUC曲线等性能评估标准来评估预测模型的性能。
在本研究中，最终分析使用了75,827名五岁以下儿童。在所提出的机器学习模型中，随机森林在所提出的分类器中总体表现最佳，准确率为96.40%，精确率为87.9%，F值为82.8%，ROC曲线为94%，召回率为78%。朴素贝叶斯的准确率也最低，准确率为87.53%，精确率为67%，F值为48%，ROC曲线为82%，召回率为53%。五岁以下儿童预防急性呼吸道感染的最重要决定因素是曾接受母乳喂养、曾接种疫苗、接触过媒体、过去两周内没有腹泻以及在医疗机构分娩。这些因素与结果变量呈正相关。
根据本研究，未接种疫苗的儿童免疫系统较弱，在撒哈拉以南非洲受急性呼吸道感染的影响很大。随机森林机器学习模型在估计急性呼吸道感染和识别风险因素方面具有更大的预测能力。这为撒哈拉以南非洲降低婴儿死亡率的政策方向提供了建议。
