Shulou(Shulou.com)06/01 Report--

Anyone who uses SPSS or MedCalc to do ROC curve, I believe many inexperienced people don't know what to do about it. Therefore, this paper summarizes the causes and solutions of the problem. Through this article, I hope you can solve this problem.

ROC curve

Basic principle of ROC curve

The ROC curve (receiver operator characteristic curve), that is, the subject working characteristic curve, is a curve drawn with the Abscissa of the diagnostic test (1-specificity) and the sensitivity as the vertical coordinate. The surface area (area under the curve, AUC) formed by the curve and diagonal can reflect the authenticity of the detection method. In general, the closer the AUC is to 1, the higher the authenticity of the detection method; the AUC is between 0.5 and 0.7, indicating that the authenticity of the detection method is low; the AUC is between 0.7 and 0.9, indicating that the authenticity of the detection method is moderate; AUC above 0.9, indicating that the authenticity of the detection method is higher.

AUC index

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