## 3 Results  
The results were evaluated using the area under the curve (_AUC_) and the Matthews correlation coefficient (_MCC_) metrics, both of which are used extensively in the fields of medical informatics and bioinformatics. The _AUC_ is the area under the receiver operating characteristic (ROC) curve plotting the true positive rate against the false positive rate [60; 61]. The Matthews correlation coefficient (_MCC_) is a statistical metric that produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset [62; 63]. The results of the experiments are listed exhaustively in Table 2 and demonstrated in Figures 1 and 2. In Figure 1, the horizontal axis contains all of the \(qi\), \(k\) value combinations, where \(qi\) is the ID of the quasi identifier set as presented in Table 2. In Figure 1, the _AUC_ and _MCC_ scores for the test dataset are plotted against \(k\) for each of the \(qi\) values, while Figure 1 depicts _AUC_ and _MCC_ plotted against \(qi\) for each of the \(k\) values. The parameters \(qi=0\), \(k=1\) represent the case in which the machine learning models were applied to the non-anonymized dataset. It can be observed that most machine learning models had better _AUC_, _MCC_ scores with the non-anonymized dataset, which means that the information loss occurring from the anonymization process in general reduced the predictive ability of the models. However, the five models were affected to various degrees, with Gaussian naive Bayes suffering the most prominent performance reduction, the decision tree and KNN classifiers showing the most stable performance across the different anonymization parameter values, and logistic regression presenting a performance improvement as a result of the anonymization process for \(qi=3\)b, 4. In Figure 2, the _AUC_ score and the _MCC_ score are plotted juxtaposed with the GIL value against the anonymization parameter values, separately for each tested model. Figure 2 depicts _AUC_, _MCC_ and _GIL_ plotted against \(k\) for each \(qi\) value separately, while Figure 2 depicts _AUC_, _MCC_ and _GIL_ plotted against \(qi\) for each \(k\) value. It can be observed that lower _GIL_ values were associated with higher _AUC_, _MCC_ values in the cases of the logistic regression and Gaussian naive Bayes models. On the other hand, the decision tree, the KNN and the SVM classifiers were less affected by the variation in the _GIL_ values. Figure 1: The results of applying five machine learning models to the dataset for various anonymization parameter combinations (_qi_, _k_), where _qi_ is the code of the quasi identifier set, and \(k\) is the minimum size of the equivalence class (Table 2). (**a**) The area under the curve (_AUC_) score results (first row) and the Matthews correlation coefficient (_MCC_) score results (second row) for the test set plotted against \(k\) for each _qi_ value. (**b**) The _AUC_ score results (first row) and the Matthews correlation coefficient (_MCC_) score results (second row) for the test set plotted against _qi_ for each \(k\) value. Figure 2: The prediction results of the tested machine learning models demonstrated with the _AUC_ and _MCC_ metrics, in juxtaposition with the GIL value. (**a**) The results of the logistic regression (LR), decision tree (DT), Gaussian naïve Bayes (GNB), k-nearest neighbors (KNN) and support vector machine (SVC) classifiers, plotted against \(k\), for each \(qi\) value. (**b**) The results of the same models plotted against \(qi\) for each \(k\) value. The demonstrated results indicate that for a specific _qi_ value, the \(k\) value did not affect the performance of the tested machine learning models significantly. Contrarily, all five models demonstrated lower performance for _qi_ = 3a, which corresponded to the _QI_ attributes (AGE, SEX, OUTCOME).