Research Paper Advance Articles

Development and validation of a nomogram for predicting stroke risk in rheumatoid arthritis patients

Model evaluation (F1-score) results based on the number of features across six models. (A) primary cohort, N = 1,354 patients; (B) validation cohort, N = 581 patients). Abbreviations: GBDT: gradient boosting decision tree; KNN: k-nearest neighbors; LR: logistic regression; RF: random forest; XGB: XGBoost; SVM: Support Vector Machine.

Figure 2. Model evaluation (F1-score) results based on the number of features across six models. (A) primary cohort, N = 1,354 patients; (B) validation cohort, N = 581 patients). Abbreviations: GBDT: gradient boosting decision tree; KNN: k-nearest neighbors; LR: logistic regression; RF: random forest; XGB: XGBoost; SVM: Support Vector Machine.