## 5 Discussion  
The previous sections presents a detailed comparative analysis of the algorithms and resampling strategies tested in this work. In this section, we will turn to the most important findings regarding the reliability of the classification. The results obtained in the experimental study conducted in the context of predictive modeling of healthcare-associated infections confirm the fact that the behavior of classification algorithms and the effectivity of resampling strategies are highly dependent on the application domain and the characteristics of the datasets. In this sense, the most remarkable aspect found in this study is that algorithms such as SVM or Bayesian networks and resampling strategies such as SMOTE yield poor results in the context of our work despite being effective in other fields. As expected, the results also show that resampling methods, which are used to address the problem of imbalanced data classification, improve precision in the classification of the minority class examples at the cost of global accuracy. Therefore, the target is to find the method that provides the best balance between those aspects. Most of the work in the literature only evaluates classic metrics such as accuracy, precision, recall and AUC, but, as shown in the empirical study, those metrics are not suitable to identify the classifiers that ensure the best balance. We have computed the values of two additional metrics, G-mean and OP, specifically designed for imbalanced data classification. Their values reveal a significant better behavior of RUS and clustering-RUS sampling strategies against SMOTE for all of the tested classifiers. The statistical significance tests also showed the superiority of clustering-RUS vs. RUS in terms of G-mean and OP for the best classifiers. Although the difference was not significant, the values of accuracy and F-measure were better for clustering-RUS. This indicates that the latter achieves the best balance between the rate of improvement in the classification of the minority class examples and the loss of accuracy. This fact is further corroborated by the subsequent analysis of all classifiers, in which we studied the loss in accuracy, OP and G-mean values with respect to the best value of each, and we evaluated the combined loss of both OP and accuracy, and G-mean and accuracy. The lowest values of combined lost were yielded by the clustering-RUS sampling strategy and the bagging-random tree classifier. This, in turn, shows that an ensemble classifier (bagging) accompanied  
Figure 14: Portion of a decision tree model. by a base classifier that is also an ensemble (random tree) are the most suitable for making predictions in the scope of our work. In addition, TPR and TNR were analyzed taking into account that the minority class is the positive. It is known that the increase in TPR produces a decrease in TNR. However, when using RUS and clustering-RUS, we found that the TPR values provided by the algorithms tested are significantly higher than those obtained when using SMOTE. By contrast, the TNR values for RUS and clustering-RUS are slightly lower than for SMOTE. The highest values of TPR were obtained for the algorithms random tree and bagging-random tree with RUS and clustering-RUS; especially when clustering-RUS was used, bagging-random tree achieved a higher value of TNR than random tree. Given the fact that the objective is to improve the classification reliability of the minority class instances without significantly decreasing the classification reliability of the majority class instances and accuracy, we can say that clustering-RUS is the most suitable approach for dealing with imbalanced data in the context of our study. The study also proves our initial hypothesis that ensemble classifiers present a better behavior than single classifiers in these situations.