Fig. 3

Construction of the diagnostic model of RHNAG. A Histograms of standardized importance scores of 54 biomarkers that are very important for diagnostic typing obtained by random forest analysis; B The histogram of the biomarkers of the top 20 importance scores was obtained by random forest analysis. The red ones are markers with an importance score greater than 50; C. ROC analysis results of 16 biomarkers. D and E The result chart of LASSO regression analysis of 10 biomarkers with AUC value greater than 0.9. The red dotted line in the middle indicates that the lambda value of our selection is 4, so we have four genes to model; F Analysis of clinical decision curve of the whole model and single molecular model. The y-axis measures the net benefit. The black line represents the non-remission risk nomogram. The thin solid line represents the assumption that all patients are in RHNAG. The thick solid line represents the assumption that all patients are in RLNAG. The decision curve shows that if the threshold probability of a patient and a doctor is > 2% and < 100%, respectively, using this RHNAG nomogram in the current study to diagnose RHNAG adds more benefit than the intervention-all scheme or the intervention-none scheme; G Diagnostic model based on the expression of four biomarkers; on the right is the score corresponding to the expression of a specific biomarker; H Calibration curves of the RHNAG diagnostic nomogram in the primary cohorts