## 6 Discussion  
When dealing with imbalanced and limited-size datasets, the evaluation of resampling and validation methods is essential to verify the stability of ML models. Our results indicated the non-ensemble DT model with data resampled with manual augmentation + SMOTE, with the best performance, obtaining a mean accuracy score of 98.99% for multiple stratified CV (see Table S2 of our Supplementary Materials) and nested CV (see Table S3 of our Supplementary Materials). The DT is followed by the multi-class AdaBoosted DTs model with a mean accuracy score of 97.99% for multiple stratified CV (see Table S2 of our Supplementary Materials) and 98% for nested CV (see Table S3 of our Supplementary Materials).  
During CKD monitoring, based on the non-ensemble DT model with data resampled with manual augmentation + SMOTE, assuming the previous DM evaluation, the user only needs to perform two blood tests: creatinine and urea periodically. Albuminuria is measured using a urine test, while GFR can be calculated using the Cockcroft-Gault equation. The reduced number of exams is relevant for developing countries like Brazil due to the high poverty levels.  
From the misclassified instances identified when testing the non-ensemble DT model, with data resampled with manual augmentation + SMOTE, the model disagreed with the experienced nephrologist, declaring very high risk rather than high risk (only one individual). However, the model did not lead to any critical underestimation of individuals' at-risk status (e.g., low risk rather than moderate risk). This situation would be a critical issue because the patient is usually referred to a nephrologist at moderate or high risk. Misleading classifications are less harmful to the patient as they still result in the patient being referred for evaluation, even if the risk is overestimated.  
Along with using a reduced number of features and the absence of critical underestimations, another advantage of the DT model is the direct interpretation of results. A more straightforward interpretation of the CKD risk analysis by nephrologists and primary care doctors who need to perform additional tests to confirm a patient's clinical status is critical to reusing the model in real-world situations. The tree generated by the DT model encompasses each CKD biomarker considered and the related classification. A doctor follows the decisions to interpret the logic of classification. Of the 8 CKD features, only 5 were used by the non-ensemble DT model with data resampled with manual augmentation + SMOTE, to classify the risk (i.e., creatinine, gender, HA, urea, and albuminuria), requiring one blood test and one urine test when DM has already been evaluated, at the cost of one misclassified instance.  
However, one of the main limitations of this study is the usage of the gridSearchCV tool to find the best parameters for each algorithm. We faced processing limitations, mainly for the ensemble models, because the parameter search was conducted for each ML model. The usage of gridSearchCV with 5 folds for the DT model is one example of such a situation. We handled 960 candidates, resulting in 4800 adjustments. However, when using the META-DES model, we handle 8640 candidates, resulting in 43,200 adjustments for the ensemble model, presenting a higher processing cost to adjust the parameters.  
Besides, the reduced amount of manually augmented instances may also be considered a limitation. For example, the number of instances for the very high risk class in the test set is too reduced, which can have a negative impact on the performance evaluation for such class. The nested CV assisted us in reducing this limitation. We did not provide more augmented data because it is a time-consuming task for the nephrologist. However, given that one of the main purposes of this study is to address limited size datasets, the manual augmentation provided by the nephrologist was enough to conduct the experiments.