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¹Clinical Biostatistician at GE Healthcare, Chicago, IL, USA. Andrew.Marra@gehealthcare.com.

|October 24, 2024

概括

在医疗器械研究中的不平衡数据集中,接收器操作特征曲线 (AUROC) 下的区域可能会误导. 一个平衡的度量家族,包括G4,P4和马修斯相关系数 (MCC),提供更可靠的绩效评估.

科学领域:

二进制分类性能指标二进制分类性能指标
医疗器械的验证和验证
机器学习在医疗保健中的应用

背景情况:

接收器操作特征曲线 (AUROC) 下的区域通常使用,但在医疗器械验证方面存在局限性.
为了更准确的绩效评估,需要使用替代指标.
作为一个平衡的度量家族 (包括P4和MCC) 的一部分,引入了一个新的度量,G4 (灵敏度,特异性,PPV,NPV的几何平均值).

研究的目的:

评估使用G4度量和平衡度量家族来分析二进制分类器性能的好处.
为了比较G4,P4和MCC与AUROC的表现,特别是在不平衡的数据集中.

主要方法:

模拟数据集的分析与不同的少数阶级流行率的模拟数据集.
包括来自对乳腺癌检测超声波AI算法的外部研究的数据.
在不同的数据不平衡场景中,AUROC与平衡指标家族 (G4,P4,MCC) 的比较.

主要成果:

当数据不平衡时,AUROC可以高估或低估分类器的性能,而平衡的指标仍然稳健.
在高度不平衡的数据中 (少数群体<10%),MCC是独立评估的首选,P4是组间分析的首选.
G4提供了一个平衡的方法,优化独立和跨组分析.

结论:

对于不平衡的数据集,AUROC提供了误导性的结果,特别是在医疗器械验证方面.
平衡度量家族 (G4,P4,MCC) 提供了对医疗器械性能更全面,更可靠的评估.
鼓励研究人员采用平衡度量家族来评估二进制分类问题.

关键词:

欧罗克 (AUROC) 是一个二元分类二元分类二元分类.G4 G4 G4 G4 的意思是什么?在MCC中,MCC是MCC,MCC是MCC.在 MRMC 中,MRMC 是一个有价值的数字.马太福音马太福音马太福音医疗器械验证医疗器械验证 P4 P4 P4 P4 P4 P4 P4 P4 P4 P4 P4 P4 P4 P4 P4 P4 P4 P4 P4 P4

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