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

Table 4 Performance of supervised models to estimate the risk of mortality in patients with dengue shock syndrome from oversampling data with the synthetic minority oversampling technique

Models	AUC (95%CI)	Sensitivity	Specificity	Precision	F1 score	Accuracy
Logistic regression	0.93 (0.89-0.97)	0.94	0.93	0.77	0.85	0.93
Naïve Bayes	0.94 (0.91-0.97)	0.95	0.94	0.80	0.87	0.94
Random forest	0.97 (0.95-0.99)	0.98	0.97	0.88	0.93	0.97
KNN	0.91 (0.87-0.95)	0.92	0.89	0.69	0.78	0.90
Decision tree	0.95 (0.92-0.98)	0.95	0.96	0.85	0.90	0.96
XGBoost	0.97 (0.95-0.99)	0.98	0.96	0.86	0.92	0.96

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