Table 7.

Performance comparison of the proposed model and previously proposed models

NASA dataset	Techniques	Precision		Recall	F-M	Accuracy
KC1	Naïve Bayes [30, 35]	86.2	74.33		35.71	65.87
	Random Forest [30]	–	75.89		37.91	67.99
	C4.5 Miner [30]	–	75.64		34.05	68.01
	Immunos[30]	–	72.91		36.92	63.55
	ANN-ABC [30]	–	77		33	69
	Hybrid self-organizing map [31]	–	80.94		35.67	78.43
	SVM [32, 35]	81.2	81.27		28.96	79.24
	Majority vote [32]	–	85.62		30.98	79.66
	AntMiner + [32]	–	84.99		26.11	80.51
	ADBBO-RBFNN [34]	–	87.95		20.24	84.96
	NN GAPO + B [36]	–	–		–	79.4
	Decision Tree[35]	83.3	94.1		87.78	86.35
	KNN[35]	83.9	84.7		84.3	–
	Proposed Model (PC-SVM)	86.8	99.6		92.8	86.6
CM1	Naïve Bayes [30, 35]	86.2	78.65		34.09	64.57
	Random Forest [30]	–	71.29		32.17	60.98
	C4.5 Miner [30]	–	74.66		27.68	66.71
	Immunos[30]	–	75.02		30.99	66.03
	ANN-ABC [30]	–	81		33	68
	Hybrid self-organizing map [31]	–	78.96		30.65	72.37
	SVM [32, 35]	81.2	79.08		31.27	78.69
	Majority vote [32]	–	80		30.46	77.01
	AntMiner + [32]	–	78.88		30.9	73.43
	ADBBO-RBFNN [34]	–	80.96		29.71	82.57
	NN GAPO + B [36]	–	–		–	74.4
	Decision Tree[35]	83.3	74.23		81.2	73.49
	KNN[35]	83.9	84.7		84.3	–
	Proposed Model (PC-SVM)	96.1	99.0		97.5	95.2