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使用机器学习技术揭示低出生体重的社会人口学决定因素

Mohammad Safi Uddin¹, Md Refath Islam², K M Ariful Kabir²

¹Directorate General of Family Planning, Ministry of Health and Family Welfare, Dhaka, Bangladesh.

PLOS global public health

|January 7, 2026

概括

识别患有低出生体重 (LBW) 风险的母亲对于儿童的生存至关重要. 机器学习模型精确地指出了点.

科学领域:

孕产妇和儿童的健康
生物统计学生物统计学
医疗保健中的机器学习

背景情况:

低出生体重 (LBW) 是一个重要的全球健康问题,特别是在孟加拉国等低收入和中等收入国家.
尽管取得了改善,但孟加拉国仍面临着持续的挑战,LBW率为14.5%,突出显示了母婴健康差异.
社会人口因素对出生体重产生了重大影响,需要进行详细的调查.

研究的目的:

确定孟加拉国LBW的关键决定因素.
开发一种机器学习 (ML) 预测模型,用于识别高风险分娩LBW婴儿的母亲.
为减少LBW患病率提供有针对性的干预措施和政策制定信息.

主要方法:

利用了2022年孟加拉国人口和健康调查 (BDHS) 的数据.
应用了各种ML算法,包括后勤回归,天真贝斯,KNN,随机森林,SVM,拉索,回归树,神经网络,XGBoost,AdaBoost和决策树.
通过火车测试分割,十倍交叉验证,准确性,精度,回忆,F1分数,R2和MSE来评估模型性能.

主要成果:

"第一次出生时的年龄"和"教育水平"被确定为LBW最重要的预测因素.
在所有测试的ML模型中,AdaBoost算法实现了最高的预测准确性.
该研究成功地确定了关键风险因素,并证明了ML在预测LBW方面的实用性.

结论:

第一次出生时的年龄和教育水平是影响LBW的关键可修改因素.
机器学习,特别是AdaBoost,为预测弱势群体的LBW风险提供了一个强大的工具.
结果可以指导公共卫生政策,以减轻LBW和改善孟加拉国母亲和儿童的结果.

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