Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques

Table 7

Confusion matrix and classification report for different machine learning classifiers.

Confusion matrix						Classification report
Models	Category	COVID-19	CAP	Normal	Total	Category	Precision	Recall	F1Score	Specificity
SVM	COVID-19	104	7	0	111	COVID-19	0.93	0.92	0.92	0.96
	CAP	7	103	2	111	CAP	0.92	0.88	0.89	0.96
	Normal	1	6	104	111	Normal	0.93	0.98	0.95	0.96
	Total	112	116	106	333	Average	0.92	0.93	0.92	0.96
Random Forest	COVID-19	106	5	0	111	COVID-19	0.95	0.97	0.95	0.97
	CAP	3	106	2	111	CAP	0.95	0.95	0.95	0.97
	Normal	0	6	105	111	Normal	0.95	0.98	0.95	0.97
	Total	109	111	107	333	Average	0.95	0.96	0.95	0.97
Decision Tree	COVID-19	104	5	2	111	COVID-19	0.93	0.93	0.93	0.96
	CAP	3	103	5	111	CAP	0.92	0.91	0.91	0.96
	Normal	4	4	103	111	Normal	0.92	0.93	0.92	0.96
	Total	111	112	110	333	Average	0.92	0.92	0.92	0.96
Naive Bayes	COVID-19	82	20	9	111	COVID-19	0.73	0.86	0.79	0.87
	CAP	8	83	20	111	CAP	0.74	0.62	0.67	0.86
	Normal	5	29	77	111	Normal	0.69	0.72	0.70	0.87
	Total	95	132	106	333	Average	0.72	0.73	0.72	0.87
KNN	COVID-19	104	7	0	111	COVID-19	0.93	0.91	0.91	0.97
	CAP	6	103	2	111	CAP	0.92	0.88	0.89	0.97
	Normal	4	6	101	111	Normal	0.90	0.98	0.93	0.96
	Total	114	117	103	333	Average	0.92	0.92	0.92	0.97