构建和评估基于机器学习的预测模型,用于早期发病的先兆子
Bohan Lv1, Gang Wang1, Yueshuai Pan2
1Department of Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Pregnancy hypertension
|January 31, 2025
概括
机器学习通过分析诸如BMI和血压等因素,有效地预测早期产前 (EOPE). XGBoost模型在识别高风险怀孕方面表现出卓越的表现.
科学领域:
- 产科和妇科 产科和妇科
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 早期产前 (EOPE) 对母亲和胎儿的健康构成重大风险.
- 确定EOPE的预测因素对于及时干预和改善结果至关重要.
研究的目的:
- 为了确定早期发作的孕前 (EOPE) 的关键影响因素.
- 为EOPE开发和验证基于机器学习的预测模型.
主要方法:
- 使用Python对1040名孕妇进行数据分析,将数据分为培训 (80%) 和测试 (20%) 集.
- 应用后勤回归,XGBoost,随机森林,支持向量机器和人工神经网络算法.
- 使用重新采样验证的模型,评估准确性,灵敏度,特异性,F1得分和AUC.
主要成果:
- 确定怀孕前的BMI,怀孕人数,平均动脉压,吸烟,α-fetoprotein和受孕方法作为重要的EOPE预测因素.
- XGBoost模型获得了最高的性能,在训练组中AUC为0.963,在测试组中为0.936.
- 在XGBoost模型中,在训练组中F1得分为0.554,在测试组中为0.488.
结论:
- 基于XGBoost的预测模型显示了EOPE的强大预测能力.
- 该模型可以作为评估孕妇EOPE风险的宝贵工具.
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