## 4 Discussion  
In this paper, we presented a series of experiments designed to explore the predictive potential of anonymized medical datasets subjected to various degrees of manipulation. The original dataset used consisted of 117,181 records derived from EHR data from a Greek hospital and nine attributes related to the patient and the hospitalization (Table 1). The data were collected in the context of the MODELHealth project, which focused on the development of a system for the extraction of electronic health record data from a hospital database, the transformation of the data through harmonization and anonymization, and their loading into a central database in order for them to be used as inputs to machine learning models [29, 30]. The dataset was subjected to one-hot decoding and anonymization using the Mondrian algorithm. Five attributes of the dataset were used as candidates for anonymization, with  
Figure 4: The AUC and MCC results of the five tested classifiers depicted through histograms (**a**,**c**) and violin plots (**b**,**d**). Figure 5: The scatterplot depicting the correlation between _GIL_ and _MCC_ (**bottom left**) and the respective densities of the _GIL_ (**upper left**) and _MCC_ (**bottom right**) metric values. four combinations of them being used to form the quasi identifier (\(Ql\)) set. In addition, seven values in the interval 2-30 were used for the \(k\) anonymization parameter, which was the minimum number of records with the same values in the \(Ql\) set (Table 2). The non-anonymized dataset as well as the 28 dataset versions resulting from the different anonymization parameter combinations were fed as input into five machine learning models, namely the decision tree, the logistic regression, the k-nearest neighbors, the support vector machine and the Gaussian naive Bayes classifiers, the parameters of which have been presented in Table 11.  
The main emerging finding is that the loss of predictive power (that is predicted class pattern consistency in a machine learning classification context) as a function of the information loss due to aggregation and suppression anonymization processes varies considerably, depending on the nature of the classification algorithm used. Indeed, in our experiments, there were classifiers that showed great resiliency even under significant information loss, and others that directly or indirectly were critically affected, with their accuracy metrics plunging towards non-significance (Figures 1 and 2). More specifically, the decision tree and the k-nearest neighbors classifiers demonstrated noticeably stable performance regardless of the anonymization parameters, as opposed to the Gaussian naive Bayes and the support vector machine classifiers. Remarkably, the logistic regression model demonstrated improved performance for various (\(qi\), \(k\)) combinations of the anonymized dataset in comparison with the non-anonymized dataset, indicating that anonymization could have a similar effect with the regularization of machine learning models, although further experiments should be performed in order to reinforce that statement. The experimental results showcased the choice of \(Ql\) set attributes as well as the GIL value resulting from the anonymization process as the factors that seem to play a more significant role regarding the prediction results, rather than the size of the \(QI\) set or the \(k\) value. Indeed, the inclusion of the OUTCOME attribute, representing the hospitalization outcome, in the anonymized attribute set resulted in performance deterioration for all tested machine learning models. Contrarily, the rest of the attributes involved in the anonymization process, which were the patient's age and sex, the number of days of the current admission, and the cumulative number of days of previous hospital admissions, did not have a similarly strong effect on the performance of the classifiers. Our study reached conclusions similar to previously published work regarding the effect of anonymization on the performance of machine learning models.