确定孟加拉国5岁以下死亡率的决定因素:使用BDHS 2022数据的机器学习方法
Shayla Naznin1, Md Jamal Uddin2,3, Ahmad Kabir2
1Department of Statistics, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.
PloS one
|June 11, 2025
概括
机器学习,特别是随机森林,准确地预测了孟加拉国5岁以下的死亡率. 关键因素包括家庭规模,财富,父母教育和产前护理访问,指导儿童生存的干预.
科学领域:
- 公共卫生 公共卫生
- 计算生物学 计算生物学
- 人口统计学 人口统计学
背景情况:
- 孟加拉国5岁以下的死亡率是关键的公共卫生和社会经济指标.
- 传统方法在模拟儿童死亡率的复杂,非线性决定因素方面面临挑战.
- 先进的机器学习 (ML) 提供了一种有前途的方法,可以更好地预测和识别关键因素.
研究的目的:
- 使用ML模型准确预测孟加拉国5岁以下的死亡率.
- 确定影响5岁以下死亡率的最重要的决定因素.
- 为改善儿童生存率提供可操作的见解.
主要方法:
- 应用多个ML算法,包括随机森林,XGBoost和神经网络到2022年孟加拉国人口健康调查数据.
- 使用Boruta算法进行特征选择.
- 使用准确度,精度,回忆,F1分数,MCC,科恩斯卡帕和AUROC评估模型性能.
主要成果:
- 随机森林 (RF) 模型在5岁以下的死亡率方面表现出卓越的预测性能.
- 射频实现了高度指标:98.75%的准确性,98.61%的回忆,98.88%的精度,98.74%的F1分数和99.79%的AUROC.
- 确定了关键预测因素:家庭规模,财富指数,父母教育,产前护理访问,出生顺序和父亲的职业.
结论:
- 随机森林模型有效地预测了5岁以下的死亡率,并确定了孟加拉国的关键决定因素.
- 研究结果强调了ML在公共卫生中的实用性,用于识别影响儿童生存的因素.
- 结果为有针对性的干预提供了数据驱动的基础,以减少儿童死亡率和改善结果.
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