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

Table 6

Comparison results of the proposed model with other classifiers after data fusion on a fused dataset.

Classifier model	Acc (%)	Pre (%)	Rec (%)	FM (%)	RMSE	MAE
Logistic regression	75.27	0.67	0.55	0.61	0.25	0.50
Naïve Bayes	74.01	0.64	0.55	0.59	0.26	0.51
Random forest	80.51	0.75	0.65	0.70	0.19	0.44
K-nearest neighbors	80.87	0.77	0.63	0.70	0.19	0.44
Decision tree	84.30	0.77	0.78	0.77	0.16	0.40
Support vector machine	84.30	0.81	0.71	0.76	0.16	0.40
Proposed model	99.64	1.00	0.99	0.99	0.00	0.06