A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning

Table 4

Comparison results of the proposed model with other classifiers before data fusion on the Pima Indians diabetes dataset.

Classifier model	Acc (%)	Pre (%)	Rec (%)	FM (%)	RMSE	MAE
Logistic regression	74.68	0.68	0.52	0.59	0.25	0.50
Naïve Bayes	72.08	0.62	0.52	0.57	0.28	0.53
Random forest	74.68	0.69	0.50	0.58	0.25	0.50
K-nearest neighbors	73.38	0.67	0.48	0.56	0.27	0.52
Decision tree	74.03	0.63	0.63	0.63	0.26	0.51
Support vector machine	74.68	0.70	0.48	0.57	0.25	0.50
Proposed model	72.73	0.63	0.56	0.59	0.27	0.52