## 3 Results
However, the performance of most models was noticeably better for _qi_ = 2, 3b, 4, values, which corresponded to the _QI_ attributes (AGE, SEX), (AGE, SEX, CURADM_DAYS) and (AGE, SEX, CURADM_DAYS, PREVADM_DAYS), respectively. This indicates that the OUTCOME input variable plays a more prominent role that the AGE, SEX, CURADM_DAYS and PREVADM_DAYS attributes regarding the prediction of readmission. It can also be deduced that the choice of the _QI_ set attributes is more significant than the size of the _QI_ set as well as the \(k\) value in terms of predictive power. In order to quantify the relationship between the _GIL_ metric and the performance metrics _AUC_, _MCC_, two linear regression models were built for each applied machine learning model and were trained on 70% of the data. The linear regression models plotted against the test data are depicted in Figure 3, and their equations are presented in Table 13.  
The statistical significance of the presented results was evaluated by performing a series of statistical tests. The descriptive statistics of the _AUC_ and _MCC_ metrics of the experiments with different ML models are presented in Table 3 and Figure 4. Both parametric one-way ANOVA analysis (Welch test, [64]), as well as non-parametric (Kruskal-Wallis, [65]), dealing with the uncertain distribution normality of the results, showed statistically significant differences among the different algorithms (_p_ < 0.001). Details of the analysis as well as pairwise comparisons are presented in Tables 14-18 of Appendix 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**95\% Confidence Interval**} & \\ \cline{3-6}  & **Classifier** & **Mean** & **Lower** & **Upper** & **Median** & **SD** \\ \hline \multirow{6}{*}{_AUC_} & DecisionTreeClassifier & 0.906 & 0.902 & 0.910 & 0.909 & 0.0111 \\  & GaussianNB & 0.583 & 0.568 & 0.597 & 0.565 & 0.0402 \\  & KNeighborsClassifier & 0.768 & 0.765 & 0.772 & 0.771 & 0.0103 \\  & LogisticRegression & 0.731 & 0.720 & 0.742 & 0.733 & 0.0311 \\  & SVC & 0.670 & 0.651 & 0.689 & 0.695 & 0.0524 \\ \hline \multirow{6}{*}{_MCC_} & DecisionTreeClassifier & 0.815 & 0.807 & 0.823 & 0.821 & 0.0217 \\  & GaussianNB & 0.252 & 0.222 & 0.283 & 0.212 & 0.0838 \\ \cline{1-1}  & KNeighborsClassifier & 0.537 & 0.530 & 0.545 & 0.542 & 0.0208 \\ \cline{1-1}  & LogisticRegression & 0.465 & 0.443 & 0.488 & 0.467 & 0.0620 \\ \cline{1-1}  & SVC & 0.363 & 0.331 & 0.395 & 0.405 & 0.0886 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The mean, median, standard deviation (SD) and 95% confidence interval of the _AUC_ and _MCC_ metric test set results of the experiments with the five tested machine learning models. Figure 3: The linear regression model representing the relationship between the performance metrics area under curve (_AUC_), Matthews correlation coefficient (_MCC_) and the generalized information loss (_GIL_) metric for all tested machine learning models. (**a**) _AUC_ as a function of _GIL_. (**b**) _MCC_ as a function of _GIL_. As expected, there was a statistically significant negative correlation between the information loss (_GIL_), as impact of the anonymization process, and the model performance (_GIL_- Test _MCC_ Pearson's \(r=-0.182\) [\(-0.020\), \(-0.335\) 95%CI], \(p=0.028\)). Figure 5 depicts the correlation scatterplot and the respective value densities.