Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome

Table 12

Outcomes assessed via specificity, recall, precision, G-measure, F-measure, MCC, and accuracy.

S. no.	Technique	Specificity	Precision	Recall	F-measure	G-measure	MCC	Accuracy
1	A1DE	0.4680	0.7404	0.8105	0.7739	0.5934	0.2935	69.1252
2	NB	0.3886	0.399	0.9540	0.5627	0.5524	0.3469	55.7461
3	MLP	0.4196	0.8438	0.7452	0.7914	0.5369	0.1437	68.2676
4	SVM	#DIV/0!	1	0.7136	0.8328	#DIV/0!	#DIV/0!	71.3551
5	KNN	0.3929	0.7139	0.7674	0.7397	0.5197	0.1675	64.1509
6	CHIRP	0.4725	0.8846	0.748	0.8106	0.5792	0.177	70.4974
7	CDT	0.4235	0.8822	0.7369	0.8031	0.5379	0.1253	69.1252
8	Forest-PA	0.4257	0.8606	0.7427	0.7973	0.5412	0.141	68.7822
9	J48	0.4453	0.8293	0.7582	0.7922	0.5611	0.1864	68.9537
10	RF	0.4454	0.8534	0.7505	0.7987	0.5591	0.1696	69.2967