Machine learning-based models for prediction of in-hospital mortality in patients with dengue shock syndrome

Table 3 Performance of supervised models to estimate the risk of mortality in patients with dengue shock syndrome without the synthetic minority oversampling technique

Models	AUC (95%CI)	Sensitivity	Specificity	Precision	F1 score	Accuracy
Logistic regression	0.92 (0.90-0.94)	0.99	0.50	0.98	0.61	0.97
Naïve Bayes	0.86 (0.70-1)	0.91	0.82	0.83	0.87	0.86
Random forest	0.86 (0.69-1)	0.82	0.91	0.90	0.86	0.86
KNN	0.86 (0.71-1)	0.82	0.91	0.90	0.86	0.86
Decision tree	0.77 (0.59-0.94)	0.82	0.73	0.75	0.78	0.77
XGBoost	0.86 (0.71-1)	0.82	0.91	0.90	0.86	0.86

AUC: Area under the curve; KNN: K-nearest neighbors; SMOTE: Synthetic minority oversampling technique; XGboost: Extreme gradient boosting.