A Proposed Technique Using Machine Learning for the Prediction of Diabetes Disease through a Mobile App

Table 3

Metrics for the performance of 10 ML algorithms using the SMOTE.

Ser.	Classifier	Accuracy (%)	Precision	Recall	F1 score
1	Logistic regression	77.0	0.63	0.70	0.66
2	Random forest	75.0	0.60	0.66	0.63
3	KNN	64.0	0.49	0.73	0.59
4	Decision tree	72.0	0.79	0.77	0.78
5	Bagging	75.0	0.81	0.81	0.81
6	AdaBoost	73.0	0.80	0.77	0.78
7	XGBoost	83.1	0.70	0.84	0.76
8	Voting	75.0	0.83	0.76	0.79
9	SVM	77.0	0.87	0.77	0.82
10	Naive Bayes	81.2	0.73	0.71	0.72