Department of Demography, College for Health, Community and Policy, The University of Texas at San Antonio, 9947 Bricewood Hill, San Antonio, TX78254, USA.
Public Health Nutr. 2022 Feb;25(2):269-280. doi: 10.1017/S1368980021004262. Epub 2021 Oct 8.
Child undernutrition is a global public health problem with serious implications. In this study, we estimate predictive algorithms for the determinants of childhood stunting by using various machine learning (ML) algorithms.
This study draws on data from the Ethiopian Demographic and Health Survey of 2016. Five ML algorithms including eXtreme gradient boosting, k-nearest neighbours (k-NN), random forest, neural network and the generalised linear models were considered to predict the socio-demographic risk factors for undernutrition in Ethiopia.
Households in Ethiopia.
A total of 9471 children below 5 years of age participated in this study.
The descriptive results show substantial regional variations in child stunting, wasting and underweight in Ethiopia. Also, among the five ML algorithms, xgbTree algorithm shows a better prediction ability than the generalised linear mixed algorithm. The best predicting algorithm (xgbTree) shows diverse important predictors of undernutrition across the three outcomes which include time to water source, anaemia history, child age greater than 30 months, small birth size and maternal underweight, among others.
The xgbTree algorithm was a reasonably superior ML algorithm for predicting childhood undernutrition in Ethiopia compared to other ML algorithms considered in this study. The findings support improvement in access to water supply, food security and fertility regulation, among others, in the quest to considerably improve childhood nutrition in Ethiopia.
儿童营养不足是一个全球性的公共卫生问题，具有严重影响。本研究通过使用各种机器学习（ML）算法，估计儿童发育迟缓的决定因素的预测算法。
本研究借鉴了 2016 年埃塞俄比亚人口与健康调查的数据。考虑了包括极端梯度提升、k-最近邻（k-NN）、随机森林、神经网络和广义线性模型在内的 5 种 ML 算法，以预测埃塞俄比亚营养不良的社会人口风险因素。
埃塞俄比亚的家庭。
共有 9471 名 5 岁以下儿童参与了这项研究。
描述性结果显示，埃塞俄比亚儿童发育迟缓、消瘦和体重不足存在显著的区域差异。此外，在 5 种 ML 算法中，xgbTree 算法的预测能力优于广义线性混合算法。最佳预测算法（xgbTree）显示，在三种结果中，存在多种不同的营养不良的重要预测因素，包括到达水源的时间、贫血史、儿童年龄大于 30 个月、出生时体重较小和母亲体重不足等。
与本研究中考虑的其他 ML 算法相比，xgbTree 算法是一种预测埃塞俄比亚儿童营养不足的合理的优越的 ML 算法。研究结果支持改善供水、粮食安全和生育调节等方面的措施，以极大地改善埃塞俄比亚儿童的营养状况。
