Table 2 Model Performance of the proposed ML models on the unseen testing set

From: Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records

Classifier	Accuracy	Precision	Sensitivity	Specificity	F1 score	AUROC	AUPRC
LR	0.7887	0.6949	0.6508	0.8575	0.6721	0.8456	0.7806
RF	0.8345	0.7746	0.7090	0.8971	0.7403	0.8637	0.8124
SVM	0.7729	0.6429	0.7143	0.8021	0.6767	0.8336	0.7740
LGBM	0.7993	0.7193	0.6508	0.8734	0.6833	0.8568	0.7834
GTB	0.7975	0.7033	0.6772	0.8575	0.6900	0.8584	0.7786
XGB	0.8345	0.7684	0.7196	0.8918	0.7432	0.8748	0.8303

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ISSN: 1756-0381

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