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. 2024 Feb 23;15:1657. doi: 10.1038/s41467-024-46043-y

Fig. 3. Machine learning-derived prediction model based on plasma metabolome for GC diagnosis.

Fig. 3

a Design of the modeling workflow. LASSO regression and random forest algorithm were adopted for feature selection and model training. The 10-DM model was validated in a test set and an external test set. The illustration was created with a full license on BioRender.com. b The Receiver operating characteristic (ROC) curve for the diagnosis of GC patients in the test set 1. A 95% confidence interval was calculated based on the mean and covariance of one thousand random sampling tests. c Contribution of the ten metabolites to the 10-DM model. d–g, The prediction performance of the 10-DM model for distinguishing GC (colored in purple) from NGC (colored in green) in the test set 1 (d) and the test set 2 (e) and for distinguishing stage I GC patients (stage IA colored in yellow and stage IB colored in brown) from NGC in the test set 1 (f) and the test set 2 (g). The dotted line represented the cutoff value of 0.50 used to separate the predicted NGC (on the left side) from GC (on the right side). Source data are provided as a Source Data file.