关于与出生相关的因素的预测分析 结果:机器学习视角
Atinuke Olusola Adebanji1, Clement Asare1, Samuel Asante Gyamerah2
1Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
机器学习通过识别胎儿心跳和妊娠年龄等关键因素,准确预测高风险怀孕. 这种方法有助于加纳和撒哈拉以南非洲减少死产和实现新生儿死亡率目标.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 孕产妇和新生儿健康
背景情况:
- 全球高死亡出生率,特别是在撒哈拉以南非洲,阻碍了实现可持续发展目标3 (SDG3) 的进展.
- 加纳在实现母婴死亡率目标方面面临挑战,包括世界卫生组织的2030年目标.
- 现有的孕产妇保健干预措施需要用高风险怀孕的预测工具来加强.
研究的目的:
- 确定影响分娩结果的关键因素 (死产与活产).
- 开发和评估用于预测高风险怀孕的机器学习模型.
- 支持加纳和其他撒哈拉以南非洲国家改善孕产妇和新生儿保健.
主要方法:
- 四个机器学习分类器的比较:极端梯度提升,随机森林,物流回归和人工神经网络.
- 利用来自加纳高等医疗机构的数据进行模型培训和验证.
- 使用合成少数群体过量采样技术 (SMOTE) 解决了分娩结果中的阶级不平衡.
主要成果:
- 胎儿心跳和出生时的妊娠年龄被确定为分娩结果最重要的预测因素.
- 母亲年龄,婴儿数量和分娩方式与分娩结果没有显著的关联.
- 随机森林模型表现出高精度 (0.98),F1得分 (0.99) 和AUC (0.90) 的卓越性能.
结论:
- 机器学习为临床环境中早期检测高风险怀孕提供了一个强大的工具.
- 该研究为改善加纳和类似地区的孕产妇和新生儿医疗保健提供了关键的见解.
- 调查结果可以为政策和研究提供信息,以加快实现全球孕产妇和新生儿健康目标的进展.
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