探索机器学习算法来预测短的分娩间隔,并确定其在东非生育年龄妇女的决定因素
Tirualem Zeleke Yehuala1, Bezawit Melak Fente2, Sisay Maru Wubante3
1Department Health informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia. sarazeleke3@gmail.com.
BMC pregnancy and childbirth
|May 9, 2025
概括
在东非,短的分娩间隔构成公共卫生风险. 机器学习模型将母亲的年龄,教育和计划生育的使用确定为关键预测因素,使得有针对性的干预措施能够改善母亲和儿童的健康.
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:
- 短的分娩间隔是东非重要的公共卫生问题.
- 这些间隔增加了对母亲和儿童健康的风险.
- 识别预测因素对于有效的干预至关重要.
研究的目的:
- 预测东非生育年龄妇女的短分娩间隔.
- 使用机器学习识别短出生间隔的关键决定因素.
主要方法:
- 利用了东非的人口和健康调查数据.
- 采用机器学习算法,包括后勤回归,决策树,随机森林和天真的贝叶斯.
- 预处理数据,执行特征选择,并使用准确性,精度,回忆,F1分数和AUC-ROC评估模型.
主要成果:
- 随机森林模型实现了最高的性能 (例如,79.4%的准确性,91.0%的回忆).
- 确定的主要预测因素包括母亲的年龄,教育状况,平等性,计划生育的使用和获得医疗保健.
- 这些因素显著影响了短的分娩间隔.
结论:
- 机器学习模型有效地预测了短的分娩间隔及其决定因素.
- 结果为有针对性的公共卫生干预提供了洞察力.
- 专注于医疗保健和计划生育的干预措施可以改善母亲和儿童的健康状况.
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