## 5 Discussion
### Wound Classification
The differences between F1 and F2 scores for both optimizers are low, while precision and recall are higher. Ultimately, the LW showed a higher overall score for all metrics and a recall of 87.6% for the F2-optimizer. However, it has the worst accuracy, which may be because LW has the lowest ratio between positive and negative classes; as such, the accuracy metric is less biased and corresponds more to reality. The better performances by the LW and CW can be interpreted by the more considerable visual variances between the positive and negative classes, where the alterations of the wounds are more visible than in DW. It also corroborates that characteristics that indicate wound alterations can differ for each wound type, confirming the need to separate the wound classification problem into three classifiers. Lastly, by comparing the performance between a single classifier and the three proposed, it can be concluded that the single classifier achieves a worse performance. Thus, it cannot be considered the final classification model. The reported results and the discussion have some essential points of information that need to be addressed. As previously mentioned, a high number of FNs can be highly prejudicial to the proposed system, causing worrying alterations to be overlooked by the clinicians. For this reason, the best ML algorithms were selected based on the F2 optimization since it gives a higher weight to the recall metric. Hence, for the CW classifier, the RF algorithm with 50 feature components had the best performance with the following hyperparameters, entropy criterion, 1000 number of estimators, and with auto maximization of features. The SVM algorithm with regularization parameter (C) of 1, with _rbf_ kernel type and an auto kernel coefficient (gamma), gave the best scores for the DW classifier with a total of 70 features. Lastly, the LW classifier utilized 60 features and elected the KNN algorithm with 5 nearest neighbors. The distance between them was measured by the _minkowski_ metric, and the uniform weight function was used to make the predictions. In summary, the LW clearly had the best performance, while the CW and DW need some improvements to obtain good predictions. As such, the LW classifier is acceptable for being implemented in the system, but overall, the classification needs improvement to be integrated into a real context. The use of oversampling techniques addresses the class imbalance problem by creating synthetic samples. However, the application of the SMOTE algorithm has to be cautious because artificially synthesized data may create unrealistic data samples that diverge from the actual dataset. Another essential consideration to consider is the lack of generalization present in the classification dataset. Besides the low amount of wounds belonging to the positive class, this reduced number is very biased because the same wound alteration is repeated for the same individual in the following images until a proposed treatment starts to have effects. To the best of our knowledge, this is the first research work that applies artificial intelligent methods to assess surgical site infection in cardiac surgery based on images collected by patients themselves in a remote patient monitoring service. Related works were not trained with our type of wounds; most of them are related to burns or ulcers, which is the reason for not being able to present a fair comparison of our results with other related work.