Systematic review and critical appraisal of predictive models for diabetic peripheral neuropathy: Existing challenges and proposed enhancements

Table 3 Comparison among different performances included in the model

Ref.	Discrimination	Sensibility	Specificity	Precision	F1 score	Recall rate	Accuracy	PPV	NPV
Ning et al[11]	AUC = 0.789 (0.741-0.873)
Metsker et al[22]	(1) AUC = 0.8922; (2) AUC = 0.8644; (3) AUC = 0.8988; (4) AUC = 0.8926; and (5) AUC = 0.8941			(1) ANN = 0.6736; (2) SVM = 0.6817; (3) Decision tree = 0.6526; (4) Linear Regression = 0.6777; and (5) Logistic Regression = 0.6826	(1) ANN = 0.7342; (2) SVM = 0.7210; (3) Decision tree = 0.6865; (4) Linear Regression = 0.7299; and (5) Logistic Regression = 0.7232	(1) ANN = 0.8090; (2) SVM = 0.7655; (3) Decision tree = 0.7302; (4) Linear Regression = 0.7911; and (5) Logistic Regression = 0.7693	(1) ANN = 0.7471; (2) SVM = 0.7443; (3) Decision tree = 0.7039; (4) Linear Regression = 0.7472; and (5) Logistic Regression = 0.7384
Wu et al[17]	D: (1) AUC = 0.656; (2) AUC = 0.724; (3) AUC = 0.731; and (4) AUC = 0.713; V; (1) AUC = 0.629; (2) AUC = 0.712; (3) AUC = 0.813; and (4) AUC = 0.830
Fan et al[21]	(1) XF: AUC = 0.847 ± 0.081; (2) CHAID: AUC = 0.787 ± 0.081; (3) QUEST: AUC = 0.720 ± 0.06; and (4) D: AUC = 0.859 ± 0.05						(1) XF: 0.783 ± 0.080; (2) CHAID: 0.757 ± 0.054; (3) QUEST: 0.766 ± 0.056; and (4) D: 0.843 ± 0.038	(1) XF: 0.642 ± 0.123; (2) CHAID: 0.680 ± 0.143; (3) QUEST: 0.716 ± 0.186; and (4) D: 0.775 ± 0.092	(1) XF: 0.882 ± 0.073; (2) CHAID: 0.807 ± 0.070; (3) QUEST: 0.805 ± 0.057; and (4) D: 0.885 ± 0.055
Zhang et al[18]	D: AUC = 0.763; V: AUC = 0.755
Li et al[10]	D: AUC = 0.858 (0.851-0.865); V: AUC = 0.852 (0.840-0.865)	0.74	0.874
Tian et al[20]	D: AUC = 0.727; V: AUC = 0.744
Li et al[15]	D: AUC = 0.8256 (0.8104-0.8408); V: AUC = 0.8608 (0.8376-0.8840)
Lian et al[16]	LR: 0.683 (0.586, 0.737); (2) KNN: 0.671 (0.607, 0.739); (3) DT: 0.679 (0.636, 0.759); (4) NB: 0.589 (0.543, 0.634); (5) RF: 0.736 (0.686,0.765); and (6) XGBoost: 0.764 (0.679, 0.801)			(1) LR: 0.687 ± 0.056; (2) KNN: 0.858 ± 0.070; (3) DT: 0.695 ± 0.032; (4) NB: 0.784 ± 0.087; (5) RF: 0.769 ± 0.026; and (6) XGBoost: 0.765 ± 0.040	(1) LR: 0.672 ± 0.056; (2) KNN: 0.559 ± 0.070; (3) DT: 0.669 ± 0.042; (4) NB: 0.378 ± 0.071; (5) RF: 0.719 ± 0.027; and (6) XGBoost: 0.736 ± 0.050	(1) LR: 0.659 ± 0.062; (2) KNN: 0.419 ± 0.073; (3) DT: 0.648 ± 0.067; (4) NB: 0.253 ± 0.061; (5) RF: 0.677 ± 0.040; and (6) XGBoost: 0.711 ± 0.066	(1) LR: 0.679 ± 0.052; (2) KNN: 0.674 ± 0.039; (3) DT: 0.682 ± 0.032; (4) NB: 0.590 ± 0.029; (5) RF: 0.736 ± 0.021; and (6) XGBoost: 0.746 ± 0.041
Liu et al[19]	AUC = 0.831 (0.794-0.868)
Wang et al[12]	AUC = 0.938 (0.918-0.958)	0.846	0.668
Zhang et al[13]	AUC = 0.647 (0.585-0.708)
Gelaw et al[14]	(1) AUC = 0.732 (0.69-0.773); and (2) AUC = 0.702 (0.658-0.746)	(1): 0.652; and (2): 0.7209	(1): 0.717; and (2): 0.577					(1) 0.384; and (2) 0.315	(1) 0.884; and (2) 0.884
Baskozos et al[23]	(1) AUC = 0.8184 (0.8167-0.8201); (2) AUC = 0.8188 (0.8171-0.8205); and (3) AUC = 0.8123 (0.8107-0.8140)

AUC: The receiver operating characteristic curve; NPV: Negative predictive value; PPV: Positive predictive value; KNN: K-nearest neighbors; LR: Linear regression; DT: Decision tree; NB: Naive bayes; RF: Random Forest; XGBoost: Extreme gradient boosting.