Table 3.

Performance comparisons between the RC–BT model with traditional machine learning algorithms

Metrics	Models
	Prediction of SFTS encephalitis					Prediction of SFTS fatality
	Decision tree	Lightgbm	SVM	Xgboost	NN	Decision tree	Lightgbm	SVM	Xgboost	NN	Scoring model
Accuracy	0.71	0.716	0.723	0.695	0.687	0.709	0.772	0.717	0.721	0.683	0.81
Sensitivity	0.598	0.611	0.541	0.612	0.631	0.644	0.535	0.578	0.632	0.558	0.745
Specificity	0.859	0.851	0.863	0.818	0.762	0.859	0.821	0.817	0.822	0.826	0.871
PPV	0.511	0.603	0.651	0.603	0.592	0.566	0.544	0.721	0.671	0.623	0.636
NPV	0.689	0.712	0.742	0.792	0.812	0.757	0.649	0.72	0.752	0.707	0.855
AUC	0.563	0.611	0.579	0.627	0.619	0.622	0.605	0.596	0.605	0.602	0.708