Table 6.

Referenced literature that considered machine-learning-based diabetic disease diagnosis.

Study	Contributions	Algorithm	Dataset	Data Type	Performance Evaluation
[76]	Diabetes and hypertension	DPM	Privately owned	Tabular	Accuracy—96.74%
[77]	Type 1 diabetes	RF	DIABIM-MUNE	Tabular	AUC—0.80
[78]	Diabetes classification	KNN	Privately owned- 4900 samples	Tabular	Accuracy—99.9%
[15]	Predict diabetic retinopathy and identify interpretable biomedical features	SVM, DT, ANN, and LR	Privately owned	Tabular	SVM (Accuracy—79.5%, AUC—0.839)
[79]	Diabetes classification	PSO and MLPNN	Privately owned	Tabular	Accuracy—98.73%