Skip to main content

Table 1 Comparison of machine learning model performance with 5-fold cross-validation

From: Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus

Classifier Accuracy Precision Sensitivity Specificity F1 score AUC
Logistic Regression 0.7610 0.7385 0.7801 0.7430 0.7587 0.8451
Random Forest 0.9402 0.9376 0.9384 0.9420 0.9379 0.9871
Support Vector Machine 0.9373 0.9310 0.9398 0.9352 0.9353 0.9829
Gradient Tree Boosting 0.9635 0.9579 0.9668 0.9606 0.9623 0.9953
Extreme Gradient Boosting 0.9618 0.9544 0.9668 0.9573 0.9606 0.9948
  1. RA: rheumatoid arthritis; SLE: systemic lupus erythematosus; AUC: area under the curve