## 5 Discussion
### Wound Classification  
Applying methods that address class imbalance improves the performance of the models. In terms of the two trials conducted to balance the class distribution, the SMOTE technique combined with undersampling achieved better overall results than the single SMOTE method. However, the best type of technique to balance data is not equal for every classifier. The SMOTE technique had better scores for the LW classifier, while for CW and DW, the SMOTE + Undersampling was better. As the number of LW samples and the ratio between classes are smaller than in other wound types, the undersampling may eliminate important information regarding the negative class. Thus, the performance is slightly lower when compared to the use of SMOTE alone. On the other hand, the CW and DW datasets are more extensive, so removing data points is less critical because there is more information regarding the majority class. Even though the oversampling techniques improved the model's performance, some flaws must be considered. SMOTE generates a lot of noisy artificial samples in the feature space, which can increase the number of data points in the boundaries between the two classes, confusing the classification algorithm. In addition, the increase in samples may result in overfitting the model. The number of features varies within each wound type classifier, suggesting that every wound type could have different representative features. Certain features may be more appropriate to characterize a specific wound type than another. However, the variance in the feature number is visible within the same type of classifier. Table 6, shows that the CW algorithm needs less number of components, 20 and 30, compared with the other types, indicating that the boundaries between the two classes are well-defined and that the alterations in the wounds are visually notorious.In contrast, DW needs more features to predict changes in the wound correctly and reports a lower performance when compared with the other classifiers. This can be interpreted as the substantial portion of the features extracted sharing similar values between the positive and negative classes, which can be explained by the few DW alterations being very hard to differentiate. Lastly, the LW algorithm varies the number of needed components from 50 to 60. It shows the best performance among the three classifiers, suggesting that the variations in the images between both classes are considerable and well-categorized. The reported metrics of the wound alteration classifier show a high standard deviation, except for accuracy, due to the high number of obtained folds for validation and testing. Hence, the selection bias is minimal, but the evaluation performance variance is considerable. As expected, the models optimized with the F1 score have more balance between precision and recall, while the optimization with the F2 score compromises the precision to obtain a higher recall. All models achieved a good accuracy, but as mentioned, it is a biased metric even after the application of oversampling because the test data is unaltered; so, there is still a superior number of samples for the negative class. From all model types, the DW classifiers had the worst performance for both metrics, while LW achieved the best scores. Regarding the F1-optimization, DW obtained poor results, below 50%, for precision, recall, F1, and F2. The performance slightly improved for the F2-score, reaching a recall of 68.4%; however, the remaining metrics, except accuracy,still showed bad results. The poor results for the DW classifier can have two possible explanations: the significant discrepancy in the number of data samples between classes in the test set and the few differences presented between characteristics of positive and negative classes. The quantity of DW that has an alteration is low, meaning there are few examples of DW alterations. In CW, the scores already surpass the 50% barrier, except for the precision for the F2-optimization.