在使用机器学习算法区分潜在结核病和活跃结核病时,VCS (体积,导电性,光散射) 参数的性能
Lijiao Chen1, Lingke Yuan2, Tingting Sun3
1Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, P.R. China.
BMC infectious diseases
|December 16, 2023
概括
机器学习模型使用白细胞VCS参数有效地区分活跃结核病 (TB) 和潜伏结核病感染. 这种方法有助于准确诊断个人结核病管理和治疗策略.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 传染性疾病 传染性疾病
- 计算生物学是一种计算生物学.
背景情况:
- 结核病 (TB) 仍然是一个重大的全球健康威胁,每年造成数百万人的死亡.
- 区分活跃结核病和潜伏结核病感染 (LTBI) 对于有效的患者管理和治疗至关重要.
- 当前的诊断方法在区分这两个状态时面临挑战.
研究的目的:
- 开发和评估机器学习模型,以区分活跃TB与LTBI.
- 在这个诊断任务中评估白细胞体积,导电性和光散射 (VCS) 参数的实用性.
主要方法:
- 收集了220名受试者 (97名活跃结核病患者,113名潜伏结核病患者) 的数据.
- 分析了46个特征,包括血液常规指标和白细胞VCS参数 (中性粒细胞,单细胞,淋巴细胞).
- 采用了四种机器学习算法:逻辑回归 (LR),随机森林 (RF),支持矢量机 (SVM) 和k-最近邻居 (KNN).
- 使用精度回调曲线下的面积 (AUPRC) 和接收器操作特征曲线下的面积 (AUROC) 评估模型性能.
主要成果:
- 在训练组中,LR和RF实现了完美的性能 (AUROC=1,AUPRC=1).
- 在训练组中,SVM和KNN也表现出了很高的表现.
- 在测试组中,LR表现最好 (AUROC=0.977,AUPRC=0.957),其次是SVM,RF和KNN.
- 所有模型都显示出强大的预测能力来区分活跃的TB和LTBI.
结论:
- 使用白细胞VCS参数的机器学习分类器对于区分活跃结核病和潜伏结核病感染非常有价值.
- 这种方法为提高结核病诊断的准确性提供了一个有希望的工具.
- 这些发现支持了在控制结核病方面采用非侵入性,数据驱动的方法的潜力.
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