Table 1. Type 2 diabetes mellitus diagnosis related studies: adopted machine learning algorithms.

Study	Instance-based	Decision trees	Neural network	Ensemble	Bayesian	Statistical model	Others
Pei et al. (2019)	Support vector machine	J48*		Adaboostm1	Naïve Bayes, Bayes net
Wu et al. (2018)						Logistic regression	K-means
Talaei-Khoei & Wilson (2018)	Support vector machine*	Decision trees	Neural network*			Logistic regression*	Clustering
Upadhyaya et al. (2017)							First-order logic rules
Nilashi et al. (2017)	Self-organizing map, support vector machine		Neural network*				Principal component analysis
Maniruzzaman et al. (2017)					Naïve Bayes	Linear discriminant analysis, Quadratic discriminant analysis	Gaussian process classification*
Kagawa et al. (2017)	Support vector machine						Rule-based*, Modified PheKB
Alghamdi et al. (2017)		J48, Decision tree, Logistic model tree		Random forest	Naïve Bayes	Logistic regression*
Esteban et al. (2017)	Support vector machine, KNN	C5.0	Neural networks*	Random forest, Gradient boosting machine, Extreme gradient boosting	Bayesian model	Linear model, Discriminant analysis, Partial least squares, Multinomial logistic regression	Rule-based, Elastic net, Nearest shrunken centroid
Anderson et al. (2015)					Bayesian inference

Note:

^*Denotes the best performed algorithm.