Table 1. Comparison of predictive model performance.

Performance statistics for 5 predictive models based on serum metabolites at the time of the first scan. The models used were logistic regression (LR) with and without interactions (I), support vector machine (SVM), random forest (RF), and decision tree (Tree). The classification accuracy (CA) represents the proportion of correctly identified cases, in contrast to specificity, which is the true negative rate. F1 is the weighted average of the precision and recall (see Methods). Statistics are rounded to 3 decimal places.

Model	F1	Precision	Recall	Specificity	CA	AUC
LR	0.630	0.708	0.567	0.860	0.750	0.802
LR + I	0.714	0.769	0.667	0.880	0.800	0.812
SVM	0.480	0.600	0.400	0.840	0.675	0.707
RF	0.542	0.722	0.433	0.900	0.725	0.779
Tree	0.464	0.500	0.433	0.740	0.625	0.630