The ROC curves and AUC values of the diagnostic model. by Zhe Wu (582498)

ROC curve and AUC value of all models. by Tiago de Oliveira Barreto (20485207)

“…As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. …”

Area under the curve (AUC) for external validation performance. by Junichi Kushioka (12236447)

Significantly Enriched Pathways. by Jiyong Zhang (2498740)

“…Pathway analysis revealed a marked decrease in expression within certain key metabolic pathways (such as the one-carbon pool by folate) in the NAFLD group, while expression in DNA repair-related pathways (such as non-homologous end joining) was significantly increased. …”

AUC of various models to predict imeglimin efficacy for liver function evaluated by ROC curves. by Naruyoshi Gyoutoku (22537831)

Column charts of WiAUC in therapy and control group at baseline and follow-up. by Felix L. Herr (21544362)

AUC ROC curve. by Dieuwke Luijten (15282736)

Statistical analysis of the AUC measurement. by Esra Ayan (21156015)

“…<p>Analysis indicated that INSv application significantly changed spontaneous calcium activity (IAsp <i>p</i>-value: 0.1290, INSv <i>p</i>-value: 0.0002). The mean AUC decreased in both samples.…”

Area under the ROC Curve (AUC) analysis to predict AIS conversion at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

Area under the ROC curve (AUC) analysis to predict neurological severity at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the fullPIERS model. by Guiyou Yang (10259597)

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the PIERS-ML model. by Guiyou Yang (10259597)

Feature importance and beeswarm plot determined by mean absolute SHAP values for the DeepSurv model. by Theophilus Gyedu Baidoo (21162511)

AUC statistics comparing statistical trends in control and test populations. by Madeline Jarvis-Cross (22394247)

“…<p>To evaluate statistical trends, we calculated Kendall’s rank correlation coefficient during the pre-critical interval (here, days one to sixty), and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger trends. …”

AUC surface with respect to regularization parameters <i>λ</i> and . by Yangsong He (13909371)

Ethnic identity as a moderator among APIDA students. by Ronna Bañada (19233063)

Feature importance and beeswarm plot determined by mean absolute SHAP values for the random survival forest (RSF) model. by Theophilus Gyedu Baidoo (21162511)

Comparative Internal and External AUC Performance Across Single-Site Models. by Ioana Duta (18462981)

“…Boxplots describe the distribution of AUC values obtained through bootstrap resampling, indicating the variance within internal and external validations. …”

The correlogram. by Bingtao Su (4740009)

“…Results demonstrated that groups with a lower prevalence of diabetes showed lower dietary EFP, while the dietary EFP and GHG emissions peaked among the prediabetes group with a prevalence of 32.7%−34.9%, and then decreased significantly with the increasing prevalence of prediabetes. …”

The food consumption quantity. by Bingtao Su (4740009)

“…Results demonstrated that groups with a lower prevalence of diabetes showed lower dietary EFP, while the dietary EFP and GHG emissions peaked among the prediabetes group with a prevalence of 32.7%−34.9%, and then decreased significantly with the increasing prevalence of prediabetes. …”