Finally, regression models to quantify the concentration of albumin in urine using the global dataset were developed. Again, the global dataset containing all Australian and Spanish dataset was split between calibration (2/3) and test (1/3) sets using the Kennard-Stone algorithm . The calibration set was used to develop and optimize PLSR, SVR and RF models that were independently tested with the test set. Since samples with high values of macroalbuminuria skewed the model and provoked high errors at lower concentrations around the low levels, which are the clinically significant ones, we opted to exclude macroalbuminuric samples directly, aiming to enhance the diagnostic accuracy within the normo/microalbuminuria range (up to 300 mg/L).