麦克罗索米亚的早期预测模型使用机器学习进行临床决策支持
Md Shamshuzzoha1, Md Motaharul Islam1
1Department of CSE, United International University, Madani Avenue, Dhaka 1212, Bangladesh.
Diagnostics (Basel, Switzerland)
|September 9, 2023
概括
预测胎儿过度生长 (宏观生长) 对母亲和婴儿的健康至关重要. 机器学习模型,特别是后勤回归,在识别高风险怀孕以及时干预方面表现有前途.
科学领域:
- 医学研究 医学研究
- 机器学习在医疗保健中的应用.
- 产科和妇科 产科和妇科
背景情况:
- 胎儿过度生长 (巨) 对母亲和婴儿构成重大健康风险.
- 准确识别高风险怀孕对于及时干预至关重要.
- 现有的研究在预测建模,机器学习集成和对宏观生物的干预有效性方面存在差距.
研究的目的:
- 开发和评估基于机器学习的模型,用于预测宏观.
- 解决当前关于宏观症预测和临床决策研究的局限性.
- 利用母亲的特征和病史来提高预测准确度.
主要方法:
- 使用母亲的特征和病史开发机器学习模型.
- 三种算法的比较:逻辑回归,支向量机和随机森林.
- 使用交叉验证选择的逻辑回归模型的超参数调整.
主要成果:
- 后勤回归算法在与支持矢量机器和随机森林相比显示出更高的性能.
- 开发的模型显示了准确预测宏观的潜力.
- 优化后勤回归模型通过交叉验证实现了高性能.
结论:
- 机器学习模型,特别是逻辑回归,可以显著提高宏观学预测.
- 改进的预测有助于在高风险妊娠中及时进行干预.
- 这种方法可以带来更好的健康结果的母亲和受影响的新生儿 macrosomia.
关键词:
临床评估 临床评估机器学习是机器学习.麦克罗索米亚 (Macrosomia) 是一个宏观的现象.新生儿的结果.肥胖 肥胖 肥胖 肥胖 肥胖 肥胖 肥胖 肥胖产科 产科 产科 产科预测建模预测建模怀孕 怀孕 怀孕 怀孕 怀孕超声波超声波是指超声波的使用.更多相关视频
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