Diabetes type 2 classification using machine learning algorithms with up-sampling technique

Journal of Electrical Systems and Information Technology

Table 3 The classification report for the selected models and datasets

Classifiers	PIMA Dataset				BRFSS Dataset
Classifiers	Diabetes	Precision	Recall	f1-score	Precision	Recall	f1-score
Extra Tree Classifier	Negative	0.92	0.87	0.89	0.99	0.94	0.96
	Positive	0.86	0.92	0.89	0.94	0.99	0.97
	Accuracy 0.89				Accuracy 0.96
	Unweighted Avg	0.89	0.89	0.89	0.97	0.96	0.96
	Weighted avg	0.89	0.89	0.89	0.97	0.96	0.96
Decision Tree Classifier	Negative	0.90	0.71	0.80	0.99	0.86	0.92
	Positive	0.75	0.92	0.82	0.87	0.99	0.93
	Accuracy 0.81				Accuracy 0.92
	Unweighted Avg	0.82	0.81	0.81	0.93	0.92	0.92
	Weighted avg	0.83	0.81	0.81	0.93	0.92	0.92
AdaBoost Classifier	Negative	0.79	0.76	0.77	0.75	0.73	0.74
	Positive	0.75	0.78	0.77	0.74	0.77	0.75
	Accuracy 0.77				Accuracy 0.75
	Unweighted Avg	0.77	0.77	0.77	0.75	0.75	0.75
	Weighted avg	0.77	0.77	0.77	0.75	0.75	0.75
Gradient Boosting Classifier	Negative	0.88	0.81	0.84	0.77	0.71	0.74
	Positive	0.81	0.89	0.85	0.73	0.79	0.76
	Accuracy 0.84				Accuracy 0.75
	Unweighted Avg	0.85	0.85	0.84	0.75	0.75	0.75
	Weighted avg	0.85	0.84	0.84	0.75	0.75	0.75