Mental stress recognition using interpretable machine learning models with heart rate variability among Chinese university students

Table 2 Performance of six machine learning classification models using heart rate variability parameters

Classifier	AUC (95%CI)	AUC	Accuracy	Precision	Recall	F1 score	Specificity
RF	0.733 (0.655-0.811)	0.733	0.689	0.705	0.665	0.675	0.712
XGBoost	0.722 (0.635-0.809)	0.722	0.670	0.676	0.678	0.667	0.662
KNN	0.711 (0.639-0.783)	0.711	0.657	0.655	0.683	0.663	0.630
LightGBM	0.700 (0.623-0.777)	0.700	0.652	0.662	0.656	0.649	0.649
SVC	0.693 (0.607-0.779)	0.693	0.659	0.658	0.677	0.660	0.640
NB	0.648 (0.566-0.730)	0.648	0.583	0.630	0.411	0.486	0.755

HRV: Heart rate variability; AUC: Area under the curve; 95%CI: 95% confidence interval; RF: Random Forest; XGBoost: Extreme Gradient Boosting; KNN: K-Nearest Neighbors algorithm; LightGBM: Light Gradient Boosting Machine; SVC: Support Vector Machine; NB: Naive Bayes.