机器学习算法可以为诊断非ST段升高心肌梗塞提供帮助
Lian Qin1, Quan Qi2, Ainiwaer Aikeliyaer1
1Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, Urumqi, Xinjiang, China.
Postgraduate medical journal
|June 9, 2023
概括
机器学习模型可以改善非ST升高心肌梗塞 (NSTEMI) 诊断. 极端梯度增强模型表现出卓越的性能,提供了一个准确的辅助诊断工具.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 诊断非ST升高心肌梗塞 (NSTEMI) 需要准确和及时的工具.
- 现有的诊断方法可以通过先进的计算方法来增强.
研究的目的:
- 开发和评估机器学习 (ML) 模型作为NSTEMI的辅助诊断工具.
- 通过使用临床和生化数据,提高NSTEMI的诊断准确度.
主要方法:
- 对2878名患者 (1409名NSTEMI,1469名不稳定性胸痛) 的回顾性分析.
- 使用SelectKBest进行特征选择,并使用特征工程来创建信息变量.
- 构建和验证六个ML模型:极端梯度增强,支向量机,随机森林,天真贝叶斯式,梯度增强机器和后勤回归.
主要成果:
- 所有六种ML模型都显示了NSTEMI的辅助诊断值.
- 极端梯度增强模型实现了最高的性能指标:精度 (0.95±0.014),精度 (0.94±0.011),回忆 (0.98±0.003) 和F-1得分 (0.96±0.007).
结论:
- 基于临床数据构建的ML模型是提高NSTEMI诊断的有效辅助工具.
- 极端梯度增强模型是基于全面评估的NSTEMI辅助诊断的最高性能算法.
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