预测患有心脏相关症状的患者的分组水平:监督机器学习方法的比较
Amirhossein Yazdi1, Mohadeseh Noori1, Seyed Mohammad Ayyoubzadeh2
1Department of Cardiology, School of Medicine, Clinical Research Development Unit of Farshchian Hospital, Hamadan University of Medical Sciences, Hamadan, Iran.
BMC emergency medicine
|December 3, 2025
概括
机器学习准确地预测心脏病患者的分拣水平,随机森林显示最佳性能. 这有助于识别高风险个体,优化紧急部门的资源配置.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 在急诊室准确的患者分组对于资源分配至关重要,特别是对于心脏病患者.
- 及时识别高风险患者可以确保及时干预和有效的医疗保健服务.
研究的目的:
- 使用机器学习预测心脏症状患者的分拣水平.
- 为了比较各种机器学习算法的性能,用于心脏病患者的分类.
主要方法:
- 一项文献审查和专家调查确定了影响分拣的关键因素.
- 从一家心脏病医院的诊断单元收集了1862名患者的患者数据.
- 应用了五种机器学习模型 (随机森林,物流回归,SVM,KNN,GB) 来进行分析.
主要成果:
- 随机森林获得了最高的准确性 (93.57%),科恩的卡帕 (0.82),和F1得分 (0.93).
- 梯度增强和SVM也表现出强的表现.
- 影响分组的关键因素包括高风险状况,需要拯救生命的干预,首席投诉和意识水平.
结论:
- 机器学习模型有效地区分低风险和高风险的心脏病患者.
- 随机森林模型为心脏病患者分拣提供了卓越的性能.
- 这些由人工智能驱动的工具可以提高急诊室对关键心脏病例的资源配置.
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