Secondly, we investigated the capability of machine learning models to discriminate samples according to its albumin content, defining two ranges: normoalbuminuria (albumin concentration <30 mg/L) or micro/macroalbuminuria (albumin concentration ≥ 30 mg/L) . Overall, this represented a more balanced (64 & 113) sample set for the calibration. Statistical values can be found in the second row of Table and Figure . This time the prediction capabilities were improved for both SVMDA and RF, achieving to classify urine samples based on their albumin excretion level with AUROC values above 0.930 for all three machine learning models (See Figure ), with RF reaching the best sensitivity and specificity values of 94.1 and 92.6, respectively. Figure -G also shows the prediction probabilities as a function of the albumin concentration, evidencing a sigmoidal distribution of the probabilities with a grey zone of diagnosis around the cut-off value.