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基于来自全血检查的分类算法的SARS-CoV-2预测策略

C F Choukhan¹, I Lasri², R El Hatimi¹

¹Laboratory of Mathematics, Computing and Applications, Mohammed V University in Rabat, Faculty of Sciences, Rabat, Morocco.

TheScientificWorldJournal

|August 31, 2023

概括

机器学习模型,支持向量机 (SVM) 和K-最近邻居 (KNN),可以使用完整的血液测试快速识别SARS-CoV-2 (COVID-19). 这种血液检测策略显示了早期传染病查的高准确性.

科学领域:

医学诊断医学诊断医学诊断
计算生物学计算生物学
传染病研究传染病研究

背景情况:

对像SARS-CoV-2这样的传染病进行准确和快速的诊断对于控制传播至关重要.
目前的方法,如RT-PCR和RDT有局限性.
从其他感染中区分COVID-19需要改进的诊断策略.

研究的目的:

训练和优化支持向量机 (SVM) 和K-近邻 (KNN) 分类器.
为了快速识别SARS-CoV-2阳性/阴性患者,使用完整的血液检测.
在没有先前的患者健康信息的情况下评估诊断性能.

主要方法:

使用SVM和KNN机器学习模型.
应用模型的患者数据从以色列人阿尔伯特·爱因斯坦,圣保罗.
分析了两个患者组:"常规病房"和"未入院".
采用ANOVA统计依赖性测试的血液样本.

主要成果:

两种SVM和KNN模型都提供了早期和准确的SARS-CoV-2检测.
优化的SVM技术在非住院患者中表现出卓越的性能.
在非住院患者中实现了高性能指标:94%的精度,96%的回忆,95%的准确性和99%的SVM AUC.

结论:

在血液资料上训练的机器学习模型为快速传染病查提供了一个有希望的策略.
这种方法可以显著改善SARS-CoV-2的初始查.
这项研究强调了完整的血液检测与人工智能相结合的公共卫生潜力.

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