使用时间序列和机器学习模型预测埃塞俄比亚的出生趋势:EDHS调查 (2000-2019) 的二次数据分析
Meron Asmamaw Alemayehu1, Amare Genetu Ejigu2, Habitamu Mekonen3
1Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Amhara, Ethiopia merryalem101@gmail.com.
BMJ open
|July 16, 2025
概括
在埃塞俄比亚,准确的出生预测对政策至关重要. 机器学习模型预测每月出生率下降,但每名妇女的出生率持续高,受人口变化和生育偏好的影响.
科学领域:
- 人口统计学 人口统计学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 埃塞俄比亚面临着影响政策的人口变化.
- 计划生育计划受到资金限制的挑战.
- 准确的出生预测对于资源分配至关重要.
研究的目的:
- 用时间序列和机器学习模型预测埃塞俄比亚未来的出生趋势.
- 为了比较各种预测模型的性能.
- 为计划生育的政策和资源分配提供信息.
主要方法:
- 利用埃塞俄比亚人口与健康调查数据 (2000-2019).
- 应用时间序列分解和分割.
- 与七个模型进行比较,包括GLMNET和Prophet-XGBoost,用RMSE,MAE和R-squared进行评估.
主要成果:
- GLMNET是最好的模型 (77%的差异解释,RMSE 119.01).
- 先知-XGBoost表现较低 (R-平方0.32,RMSE 146.87) 的性能.
- 预测预测每月出生数量下降和波动,但在10年内每名妇女的出生数量增加.
结论:
- 时间序列和机器学习模型的结合对于出生预测是有效的.
- 埃塞俄比亚的平均月产预计将下降,而每名妇女的平均分娩率仍然很高.
- 调查结果强调了需要政策解决人口趋势和社会文化因素.
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