Selected article for: "area ROC curve and AUC ROC curve"

Author: Yunbao Pan; Guangming Ye; Xiantao Zeng; Guohong Liu; Xiaojiao Zeng; Xianghu Jiang; Jin Zhao; Liangjun Chen; Shuang Guo; Qiaoling Deng; Xiaoyue Hong; Ying Yang; Yirong Li; Xinghuan Wang
Title: Can routine laboratory tests discriminate 2019 novel coronavirus infected pneumonia from other community-acquired pneumonia?
  • Document date: 2020_2_25
  • ID: dy28eo89_12
    Snippet: Statistical analyses were conducted using IBM SPSS version 22.0 software. Statistical analysis for the results was performed using the Mann-Whitney U test for only two groups or using one-way analysis of variance when there were more than two groups. The receiver operating characteristic (ROC) curves were generated and the area under the ROC curve (AUC) was measured to evaluate the discriminative ability [10] . Higher AUC were considered to show .....
    Document: Statistical analyses were conducted using IBM SPSS version 22.0 software. Statistical analysis for the results was performed using the Mann-Whitney U test for only two groups or using one-way analysis of variance when there were more than two groups. The receiver operating characteristic (ROC) curves were generated and the area under the ROC curve (AUC) was measured to evaluate the discriminative ability [10] . Higher AUC were considered to show better discriminatory ability as follows: excellent, AUC of ≥0.90; good, 0.80 ≤ AUC<0.90; fair, 0.70 ≤ AUC< 0.80. A p-value <0.05 was considered statistically significant.

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