评估低出生体重的社会经济和人口风险因素,使用模型不可知的可解释组合
Md Amir Hamja1, Mahmudul Hasan2, Maknun Jahan3
1Department of Statistics, Hajee Mohammad Danesh Science and Technology University, Dinajpur, 5200, Bangladesh.
Computer methods and programs in biomedicine
|March 5, 2026
概括
这项研究开发了一个AI框架,使用机器学习和可解释的AI来预测低出生体重 (LBW). 该模型准确地确定了影响LBW的关键社会经济和人口因素,有助于针对性干预.
科学领域:
- 公共卫生 公共卫生
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 低出生体重 (LBW) 是与新生儿并发症相关的全球关键健康问题.
- 早期预测LBW对于降低死亡率和实现有针对性的干预至关重要.
研究的目的:
- 开发一个使用机器学习 (ML),深度学习 (DL) 和可解释AI (XAI) 的预测框架.
- 确定LBW的主要社会经济和人口统计因素.
主要方法:
- 来自孟加拉国人口和健康调查 (BDHS) 的数据分析,共有1574名参与者.
- 开发一个堆叠组合模型 (SmartFusion-LR5),结合多个ML算法.
- 使用准确度,精度,回忆,AUC,F1得分和MCC来评估模型性能.
主要成果:
- 智能Fusion-LR5模型实现了高性能:93.0%的精度,99.8%的回忆率和94.0%的AUC.
- 显著的LBW患病率差异与地理划分,父母教育和社会经济地位有关.
- 可解释的人工智能确定了母亲第一次出生时的年龄,分裂,居住地,财富和丈夫的教育作为关键决定因素.
结论:
- 拟议的框架为早期LBW风险预测提供了一个强大的和可解释的方法.
- 这种方法支持在资源有限的环境中针对母婴健康的干预措施.
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