A Novel Approach for Feature Selection and Classification of Diabetes Mellitus: Machine Learning Methods

Table 5

Classification accuracy of different methods with literature.

Authors	Data size	Techniques	Classification accuracy (%)
Li et al. [39]	768	Ensemble of SVM, ANN, and NB	58.3
Deng and Kasabov [40]	768	Self-organizing maps	78.40
Brahim-Belhouari and Bermak [16]	768	NB, SVM, DT	76.30
Smith et al. [41]	768	Neural ADAP algorithm	76
Choubey et al. [2]	768	Ensemble of RF and XB	78.9
Quinlan et al. [42]	768	C4.5 Decision trees	71.10
Bozkurt et al. [43]	768	Artificial neural network	76.0
Parashar et al. [44]	768	SVM, LDA	77.60
Sahan et al. [45]	768	Artificial immune System	75.87
Chatreti et al. [46]	768	Linear discriminant analysis	72
Christobel and Sivaprakasam [47]	460	K-nearest neighbour	78.16
Smith et al. [41]	768	Ensemble of MLP and NB	64.1
Proposed method	768	KNN, RF, DT, MLP	79.8