Table 10.

Comparison of our model with similar works for COVID-19 prediction

Dataset	Reference	Model	Accuracy (%)	Precision (%)	Sensitivity (%)	Specificity (%)	F1-score (%)	AUC (%)
Dataset 1	Our model	$\mathbf{NSGA}-II+\mathbf{AdaBoost}$	85	90	89.32	85	86.01	87.16
	Banik et al. [32]	Logistic Regression	81.2	79.7	79.7	–	79.7	–
		Naive Bayesian	75.9	73.9	73.9	–	73.9	–
		Decision Tree	71.9	70.4	67.3	–	68.8	–
		LinearSVM	80.2	77.6	80.4	–	85	–
		Random Forest	80.6	77.8	84	–	80.8	–
Dataset 2	Our model	$\mathbf{NSGA}-II+\mathbf{AdaBoost}$	95.56	95.56	95.56	98.19	–	96.87
	Zoabi et al. [30]	Gradient boosting	–	–	87.3	71.98	–	90

Bold values highlight the best results for the two studied datasets