Fig. 5.

Screening for potential biomarkers through machine learning. A Based on the decision tree model, the fit model is screened. B Using the Catboost model, the importance of each protein under this condition is obtained. C The AUCs of different potential biomarker combinations in the Catboost model. D There is a good trend of separation in various models. The classification performance of candidate biomarker combinations on the four classical models is evaluated. E Validation of potential marker combinations using ROC curves. The sensitivity of ROC in the training set is 1, and the sensitivity in the test set is 0.88.