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

Table 2 Characteristics of the best-performing model from each included study

Ref.	Number	Algorithm type	Missing data imputation	Oversample	Train-test split	Validation type	Internal validation method	Best model	Prediction horizon, year	AUC (95%CI)
Cho et al[22], 2008	292	ML	NA	NA	NA	Internal	LOOCV	SVM	1	0.969 (0.941-0.997)
Dagliati et al[23], 2018	943	ML	missForest	Oversampling	NA	Internal	LOOCV	LR	3	0.808 (0.772-0.845)
Rodriguez-Romero et al[24], 2019	10251	ML	NA	SMOTE	66:34	Internal	CV-10	RF	1-1.9	0.730 (0.715-0.745)
Allen et al[25], 2022	111046	ML	missForest	NA	7:1	External	Holdout	XGBoost	5	0.750 (0.734-0.766)
Dong et al[26], 2022	861	ML	missForest	NA	8:2	Internal	CV-5	LightGBM	3	0.815 (0.747-0.882)
Nicolucci et al[27], 2022	147664	ML	Extra-values imputation	SMOTE	Yes	External	CV-10	XGBoost	2	0.970 (0.968-0.972)
Sabanayagam et al[28], 2023	1365	ML	Mean/mode	NA	8:2	Internal	CV-5	Elastic Net	6	0.851 (0.847-0.856)
Hosseini Sarkhosh et al[29], 2023	3444	ML	Mean/mode	NA	8:2	External	RFECV	RF	5	0.790 (0.770-0.820)
Yun et al[30], 2024	6040	DL	NA	NA	7:3	Internal	Holdout	LSTM	5	0.830 (0.807-0.853)
Lin et al[31], 2025	3291	ML	MICE	NA	7:3	Internal	Holdout	SuperLearner	2.53	0.714 (0.673-0.755)
Zou et al[32], 2025	12190	ML	MICE	SMOTE	8:2	Internal	CV-5	LightGBM	3	0.918 (0.906-0.930)
Dei Cas et al[33], 2025	32379	DL	MICE	NA	8:2	External	CV-5	RNN	2	0.887 (0.869-0.904)

AUC: Area under the receiver operating characteristic curve; ML: Machine learning; NA: Not available; LOOCV: Leave-one-out cross-validation; SVM: Support vector machine; LR: Logistic regression; SMOTE: Synthetic minority oversampling technique; CV-10: 10-fold cross-validation; RF: Random forest; CV-5; 5-fold cross-validation; RFECV: Recursive feature elimination with cross-validation; DL: Deep learning; LSTM: Long short-term memory; RNN: Recurrent neural network.