Independent testing statistics available in Table indicated that the incorporation of the integrated dataset improved the classification performance for both DKD vs. control and normo vs. micro/macroalbuminuria. Notably, the choice of the ML method became less crucial as in this case all perform more similarly. For the DKD (See Figure ) classification, PLSDA and SVMDA obtained AUROC, sensitivity and specificity values were found to be above 0.99, 98.4% and 93.3%, respectively. . Analytical figures of merit for the independent validation of the different machine learning models for the global classification. Note: AUROC stands for area under the receiver operating characteristic.