机器学习方法用于预测五岁以下儿童死亡率的应用:对尼日利亚人口健康调查2018年数据集的分析
Oduse Samuel1, Temesgen Zewotir2, Delia North2
1School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, 4001, Durban, South Africa. 213571335@stu.ukzn.ac.za.
BMC medical informatics and decision making
|March 26, 2024
概括
机器学习准确地预测了尼日利亚的五岁以下儿童死亡率 (U5M). 关键预测因素包括财富,母亲教育和产前访问,为减少儿童死亡率提供有针对性的干预措施提供信息.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 人口统计学 人口统计学
背景情况:
- 五岁以下的死亡率 (U5M) 是发展中国家的一个关键公共卫生挑战.
- 尼日利亚在降低儿童死亡率方面面临重大挑战.
研究的目的:
- 评估机器学习算法来预测尼日利亚五岁以下儿童死亡率.
- 确定尼日利亚背景下五岁以下儿童死亡率的关键决定因素.
主要方法:
- 利用了2018年尼日利亚人口与健康调查的数据.
- 评估了多种机器学习模型,包括随机森林,人工神经网络和物流回归.
- 性能指标包括准确性,AUROC,精度和F-测量.
主要成果:
- 随机森林和人工神经网络模型显示出高预测准确度 (89.47%) 和AUROC (96%).
- 确定了五岁以下儿童死亡率的重要预测因素:财富指数,母亲教育,产前访问,分娩地点,母亲就业,儿童数量和地区.
- 根据人口统计和社会经济因素,五岁以下儿童的死亡率有很大差异.
结论:
- 机器学习模型为准确预测尼日利亚五岁以下儿童死亡率提供了强大的工具.
- 解决社会经济,人口和医疗保健准入差距对于减少U5M至关重要.
- 结果可以指导政策制定者和医疗保健专业人员制定有针对性的干预措施.
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