Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes

Table 5

Comparison of predictive accuracies of proposed and other classifiers for Pima Diabetes.

Reference	Approach	Predictive accuracy
This Study	HFS + WLSTSVM	89.71%
Polat et al. [48]	LSSVM	78.21%
Temurtas et al. [36]	MLNN with LM	82.37%
Kahramanli and Allahverdi [49]	Hybrid system	84.2%
Statlog [47]	Logdisc	77.7%
Jankowski [50]	IncNet	77.6%
Weka [47]	Logistic	77.08%
Statlog [47]	SMART	76.8%
Weka [47]	Naïve Bayes	76.04%
Weka [47]	SMO	76.43%
Statlog [47]	BP	75.2%
Ster and Dobnikar [27]	ASI	76.6%
Ster and Dobnikar [27]	MLP + BP	76.4%
Ster and Dobnikar [27]	Fisher disc. analysis	76.5%
Ster and Dobnikar [27]	QDA	59.5%
Ster and Dobnikar [27]	KNN	71.9%
Ster and Dobnikar [27]	CART	72.8%
Zarndt [47]	Bayes	72.2 ± 6.9
Zarndt [47]	C4.5 DT	72.7 ± 6.6
Zarndt [47]	ID3	71.7 ± 6.6
Zarndt [47]	IB3	71.7 ± 5.0
Zarndt [47]	OCN2	65.1 ± 1.1