Machine learning approach for predicting under-five mortality determinants in Ethiopia: evidence from the 2016 Ethiopian Demographic and Health Survey
Machine learning approach for predicting under-five mortality determinants in Ethiopia: evidence from the 2016 Ethiopian Demographic and Health Survey
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DOI：
10.1186/s41118-020-00106-2
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发表时间：
2020-11-04
期刊：
影响因子：
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通讯作者：
Sparks, Corey S.
中科院分区：
文献类型：
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作者：
Bitew, Fikrewold H.;Nyarko, Samuel H.;Sparks, Corey S.
关于使用机器学习模型预测埃塞俄比亚五岁以下儿童死亡率风险的文献很少。在这项研究中，我们展示了埃塞俄比亚五岁以下儿童死亡率的空间变化，并使用机器学习模型来预测其重要的社会人口决定因素。研究数据来自2016年埃塞俄比亚人口与健康调查。我们使用了三种机器学习模型，如随机森林、逻辑回归和K-最近邻，以及一种传统的逻辑回归模型来预测五岁以下儿童死亡率的决定因素。对于每种机器学习模型，使用模型准确性和受试者工作特征曲线的测量来评估每种模型的预测能力。描述性结果表明，埃塞俄比亚五岁以下儿童死亡率存在相当大的区域差异。五岁以下儿童死亡率的预测能力被认为是46.3%和67.2%之间的模型，随机森林模型（67.2%）表现出最好的性能。最佳预测模型表明，家庭规模、获得水源的时间、母乳喂养状况、前5年的出生次数、儿童性别、生育间隔、产前护理、出生顺序、水源类型和母亲的体重指数对埃塞俄比亚5岁以下儿童死亡率起着重要作用。随机森林机器学习模型在估计五岁以下儿童死亡风险因素方面具有更好的预测能力，可能有助于改善这方面的政策决策。通过利用这些重要因素为相关政策提供信息，可以大大提高儿童的生存机会。
There is a dearth of literature on the use of machine learning models to predict important under-five mortality risks in Ethiopia. In this study, we showed spatial variations of under-five mortality and used machine learning models to predict its important sociodemographic determinants in Ethiopia. The study data were drawn from the 2016 Ethiopian Demographic and Health Survey. We used three machine learning models such as random forests, logistic regression, and K-nearest neighbors as well as one traditional logistic regression model to predict under-five mortality determinants. For each machine learning model, measures of model accuracy and receiver operating characteristic curves were used to evaluate the predictive power of each model. The descriptive results show that there are considerable regional variations in under-five mortality rates in Ethiopia. The under-five mortality prediction ability was found to be between 46.3 and 67.2% for the models considered, with the random forest model (67.2%) showing the best performance. The best predictive model shows that household size, time to the source of water, breastfeeding status, number of births in the preceding 5 years, sex of a child, birth intervals, antenatal care, birth order, type of water source, and mother's body mass index play an important role in under-five mortality levels in Ethiopia. The random forest machine learning model produces a better predictive power for estimating under-five mortality risk factors and may help to improve policy decision-making in this regard. Childhood survival chances can be improved considerably by using these important factors to inform relevant policies.