Table 7.

Comparison of previous studies that used machine learning algorithms to predict DPSN in type 2 diabetes mellitus patients.

References	Criteria to Diagnose DSPN	Suggested ML Models	AUC/Accuracy	Laboratory Data Processing	Providing Decision-Making Tool
Kazemi et al., 2016 [24]	clinical (T1DM and T2DM)	MSVM	UC/0.76	UC	N
Dagliati et al., 2018 [25]	UC	LR	0.726/0.746	UC	nomogram
Fan et al., 2021 [27]	UC	EM	0.847/0.783	UC	N
Maeda-Gutierrez et al., 2021 [38]	clinical	RF	0.65/UC	UC	N
Current study	electrophysiological	RF	0.825/0.7447	average/change pattern	decision tree

Abbreviations: ML = machine learning, AUC = area under the curve; MSVM = multicategory support vector machine; LR = logistic regression; EM = ensemble model; RF = random forest; UC = uncheckable; N = none.