Machine learning and deep learning in predicting the risk of diabetic kidney disease: A systematic review and meta-analysis

Table 3 Subgroup analysis of pooled area under the receiver operating characteristic curve values in internal validation

Subgroup variable	Level	Studies	AUC (95%CI)	I2	t11	P value	χ2	P value
Region	Overall				7.34	< 0.0001	0.01	0.9422
	Asian	7	0.86 (0.74-0.92)	96.7%
	Western	5	0.86 (0.78-0.91)	99.7%
Study type	Overall	12	0.86 (0.78-0.91)	85.8%	7.34	< 0.0001	0.47	0.4951
	Prospective study	3	0.90 (0.40-0.99)	99.7%
	Retrospective study	9	0.85 (0.74-0.91)	99.7%
Algorithm type	Overall				7.34	< 0.0001	0.00	0.9563
	ML	10	0.86 (0.76-0.92)	99.7%
	DL	2	0.86 (0.23-0.99)	99.7%
Center	Overall	12	0.86 (0.78-0.91)	99.7%	7.34	< 0.0001	0.02	0.8770
	Single-center	5	0.86 (0.66-0.96)	97.7%
	Multicenter	7	0.85 (0.78-0.91)	99.8%
Validation	Overall	12	0.86 (0.78-0.91)	99.7%	7.34	< 0.0001	0.26	0.6107
	Internal validation	8	0.84 (0.74-0.91)	98.5%
	External validation	4	0.86 (0.78-0.91)	99.9%
Prediction horizon	Overall	12	0.86 (0.78-0.91)	99.7%	7.34	< 0.0001	0.92	0.3362
	< 3 years	5	0.90 (0.64-0.98)	99.8%
	≥ 3 years	7	0.83 (0.77-0.88)	98.0%

AUC: Area under the receiver operating characteristic curve; CI: Confidence interval.