使用机器学习模型预测母亲的风险水平
Sulaiman Salim Al Mashrafi1,2, Laleh Tafakori3, Mali Abdollahian3
1School of Science, RMIT University, Melbourne, Victoria, Australia. S3912607@student.rmit.edu.au.
BMC pregnancy and childbirth
|December 18, 2024
概括
机器学习模型可以预测母亲的健康风险. 随机森林模型在识别高风险怀孕方面表现最好,有助于早期干预以减少孕产妇死亡率.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 孕产妇发病率和死亡率是关键的全球卫生问题,降低孕产妇死亡率 (MMR) 是可持续发展目标 (SDG) 的一个关键目标.
- 准确预测孕产妇健康风险对于有针对性的干预措施至关重要,但仍然具有挑战性.
- 机器学习 (ML) 提供了一种有前途的方法,用于开发准确的预测模型,以预测母亲的健康结果.
研究的目的:
- 探索各种ML算法的有效性,以预测孕产妇的风险水平.
- 利用全国性的阿曼孕产妇死亡数据集用于基于ML的风险预测.
- 为数据驱动的战略奠定基础,以减轻孕产妇死亡率.
主要方法:
- 利用了阿曼402例孕产妇死亡数据集 (1991-2023年).
- 应用并比较了十个ML算法,包括随机森林 (RF),有和没有主要组件分析 (PCA).
- 使用准确度,灵敏度,精度和F1分数等指标评估模型性能.
主要成果:
- 随机森林 (RF) 模型在应用PCA后,在预测孕产妇风险水平方面表现优异.
- 优化的射频模型在风险分类方面实现了75.2%的准确性,85.7%的精度和73%的F1得分.
- 这表明ML的潜力,特别是RF,在准确识别高风险的孕产妇病例.
结论:
- 机器学习模型成功地应用到使用阿曼数据来预测母亲的风险水平.
- 随机森林算法被证明是这个分类任务中最有效的.
- 准确的孕产妇风险预测可以显著帮助医疗保健提供者制定及时的干预计划,以减少孕产妇死亡率.
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