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0 decision tree combined with the synthetic minority oversampling technique (SMOTE) using five variables had the highest area under the receiver operating characteristic curve (AUC 0.807 [95% CI 0.724-0.889]), a better performance than the nomogram (AUC 0.69 [95% CI 0.594-0.787]). The results support potential clinical application of machine learning for patient risk classification. To assess the treatment benefit of eyes with diabetic macular edema (DME) in vitrectomized eyes for tractional complications of proliferative diabetic retin
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