Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome

Table 8

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	0.5897	0.2276	0.4583	0.3041	0.5158	0.0396	56.2319
2	NB	0.7018	0.7655	0.4805	0.5904	0.5704	0.1737	55.3623
3	MLP	0.7257	0.5724	0.6975	0.6288	0.7113	0.4075	71.5942
4	SVM	0.5814	0.0069	1	0.0137	0.7353	0.0633	58.2609
5	KNN	0.6818	0.5655	0.5578	0.5616	0.6136	0.2401	62.8986
6	CHIRP	0.7225	0.5655	0.6949	0.6236	0.7084	0.4011	71.3043
7	CDT	0.6516	0.4138	0.5941	0.4878	0.6215	0.2265	63.4783
8	Forest-PA	0.7017	0.5103	0.6916	0.5873	0.6966	0.3685	69.8551
9	J48	0.7018	0.531	0.6581	0.5878	0.6792	0.3452	68.6957
10	RF	0.743	0.6207	0.687	0.6522	0.7139	0.4228	72.1739