[Retracted] IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Table 3

Comparison of statistical measurement for various classification techniques.

	Logistic regression		K-nearest neighbor		Support vector machine		Proposed model
	Collected dataset	Pima dataset	Collected dataset	Pima dataset	Collected dataset	Pima dataset	Collected dataset	Pima dataset
Accuracy	0.872	0.744	0.739	0.708	0.888	0.744	0.984	0.750
Error	0.127	0.255	0.261	0.291	0.112	0.255	0.016	0.250
Sensitivity	0.923	0.775	0.778	0.748	0.898	0.775	0.987	0.789
Specificity	0.764	0.666	0.702	0.603	0.816	0.666	0.916	0.661
Precision	0.885	0.856	0.816	0.832	0.933	0.856	0.991	0.840
F-measure	0.903	0.813	0.797	0.787	0.915	0.813	0.989	0.813
MCC	0.732	0.416	0.503	0.331	0.764	0.416	0.963	0.436
Kappa	0.727	0.470	0.516	0.419	0.713	0.466	0.922	0.488
AUC	0.908	0.765	0.916	0.815	0.893	0.771	0.984	0.978