## 2 Related Work
The implantation cases in the dataset were far fewer than the no-implantation ones; thus, the authors address the great imbalance of data simply by building the training and the test sets with the same proportion of cases of the minority and the majority class as in the original dataset. However, no further treatment is performed. The impact of SMOTE on the performance of three well-known classifiers (probabilistic neural network, naive Bayes, and decision tree) is analyzed in a study concerning diabetes prediction [39]. For the three algorithms, sensitivity increased when the dataset was widely oversampled, but as expected, accuracy and specificity decreased in all cases. Work specifically aimed at the study of healthcare-associated infections is scarcer. There are works in the literature in which different machine learning techniques are applied to predict infections but in most of them the problem of imbalanced data is not addressed. One of these works is presented in [4], where a case-based reasoning (CBR) model is used to make automatic diagnostics of HAIs. The system, which includes expert-defined and automatically generated rules, achieve an accuracy degree similar to that of the experts. Therefore, it is required domain knowledge to obtain some of the rules, which is a major drawback  
Resampling strategies for generating synthetic instances were applied in a prevalence study of nosocomial infections [40]. It involves another strategy based on asymmetrical soft margin for SVM. The purpose of the study is the automatic identification of patients with a high risk of acquiring HAIs, facilitating in this way the time-consuming task of infection surveillance and control. The results prove that both approaches improve sensitivity values, but receiver operator characteristic (ROC) analysis is not performed. A more recent paper [41] analyses the incidence and risk factors of healthcare-associated ventricilities and meningitis in a neuro-ICU by means of tree-based machine learning algorithms. The problem of imbalanced data is addressed by preprocessing data with the SMOTE resampling technique. The work is mainly focused on analyzing risk factors and feature selection. The results showed a better performance of tree-based algorithms than regression models. Another additional advantage pointed out by the authors is the fact that the first methods allow the identification of interaction between factors, and that information can be used to take preventive measures. Risk factors selection was improved by combining the results from relative risk analysis, regression and machine learning methods. After analyzing different approaches to address the problem of classification from imbalanced data, we can conclude that there is no fully satisfactory and widely applicable solution. Each strategy has its advantages and disadvantages, and their respective effectiveness depends on multiple factors. Oversampling methods are characterized by the problem of overfitting, which reduces the applicability of the classification model. Some proposals have been made to improve SMOTE, one of the most popular oversampling strategies, through the treatment of noise and borderline examples, but the overfitting drawback persists. Generative adversarial networks have emerged as new and promising oversampling techniques, although their application is practically limited to the field of image processing. Their high computational cost is one of their main drawbacks. Undersampling techniquesdo not are affected by overfitting problems, but rather from a loss of information due to the elimination of instances. The improvements with respect to the basic method, RUS, also focus on the treatment of borderline examples and noise, as well as on the selection of the most representative points of the majority class. These strategies generally perform worse than oversampling; therefore, they are often applied in combination.