不同机器学习分类模型的比较,用于预测下肢骨折的深静脉血栓形成
Conghui Wei1, Jialiang Wang1, Pengfei Yu1
1Department of Rehabilitation Medicine, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China.
Scientific reports
|March 23, 2024
概括
机器学习模型可以有效地预测下肢骨折患者的深静脉血栓形成 (DVT). 极端梯度增强 (XGBoost) 模型在早期DVT检测方面表现出卓越的性能.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 血管外科 血管外科
背景情况:
- 深静脉血栓症 (DVT) 是下肢骨折后的重大并发症,影响患者的康复和生活质量.
- 早期预测和预防DVT对于改善患者的预后和结果至关重要.
研究的目的:
- 构建和评估各种机器学习模型,以确定它们在患有下肢骨折的患者中预测DVT的有效性.
- 在这个患者队列中确定最有效的机器学习模型用于早期DVT检测.
主要方法:
- 开发了五种机器学习模型:极端梯度提升 (XGBoost),物流回归 (LR),随机森林 (RF),多层感知器 (MLP) 和支持向量机器 (SVM).
- 模型性能使用包括曲线下面面积 (AUC),准确度,灵敏度,特异性,F1得分和Kappa等指标进行评估.
主要成果:
- XGBoost 模型以 0.979 的 AUC 和 0.931.91 的精度实现了最高的性能.
- 随机森林模型也表现出强的性能 (AUC=0.970,精度=0.921).
- 其他模型,包括LR,MLP和SVM,具有较低的预测能力.
结论:
- 在研究的数据集中,XGBoost模型显示了DVT预测的最佳性能.
- 在临床应用XGBoost模型用于DVT预测之前,需要进一步的外部验证.
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