## 3 Results
At the same time, the Youden index approximately receives its maximum value (0.3205). On the other hand, the misclassification error is 33.44%, which is higher than its minimum value (12.44%), corresponding to prediction probability threshold equal to 0.035 (which is clearly, however, very close to zero). The reason for this is that in the test dataset, the class imbalance in operation success still exists, thus, decrease in sensitivity results in higher values of misclassification error, despite the larger increase in the specificity. Assuming that it is of equal interest to correctly predict both the successful and unsuccessful operation results, a slight increase of the misclassification error is acceptable provided that high values of specificity are achieved. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Accuracy Measures MBFS**} \\ \hline Overall Accuracy & 0.6647 \\ \hline Sensitivity & 0.6662 \\ \hline Specificity & 0.6543 \\ \hline AUROC & 0.7145 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy measures for the MBFS model over the test dataset (\(n=5210\)). The values of overall accuracy, sensitivity, specificity, and area under the receiver operation characteristics (AUROC) curve are displayed (the selected threshold for the predicted probabilities was set to 0.5). Figure 3: Diagram of the sensitivity, specificity, Youden index, and misclassification error for the MBFS model across different prediction probability thresholds, based on the test dataset (\(n=5210\)). The results of the comparison among the MBFS model and the models JCTO, CASTLE, RECHARGE, CL, and PROGRESS are shown in Table 3 and Figure 4. In Table 3, the number of the features is displayed for each model, along with their specific names, the AIC value obtained based on the corresponding logistic regression model (downsampled training data), and the AUROC value (test data). The differences among the models are small, which has already been noted, among else, in [17]. The proposed MBFS model is the best one in terms of both the AIC (exhibited minimum value) and the AUROC (exhibited maximum value), compared to all the remaining models. Particularly, the MBFS model exhibited an AIC value of 3751.7, compared to (in increasing order) 3760.6 (JCTO), 3774 (CASTLE), 3774.1 (RECHARGE), 3841.7 (CL), and 3915.1 (PROGRESS). At the same time, the AUROC with the MBFS model was 0.715, compared to (in decreasing order) 0.710 (CASTLE), 0.709 (RECHARGE), 0.708 (JCTO), 0.706 (CL), and 0.659 (PROGRESS). Note that this is not a strict ranking of the above models, since the JCTO, CASTLE, RECHARGE, CL, and PROGRESS models are used in a different way than in the original articles, and mainly as feature selection tools in this study. In Figure 4, it is visually exhibited that the AUROC corresponding to the MBFS model surpasses all other models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **\# of** & & **FEATURES USED** & **AIC** & **AUROC** \\
**Name** & **Features** & & & \\ \hline MBFS & 5 & \begin{tabular}{c} Segmental, Proximal tortuosity, \\ Calcification, Stump, CTO length \\ \end{tabular} & 3751.7 & 0.715 \\ \hline CASTLE & 6 & \begin{tabular}{c} Previous CABG, Age, Proximal \\ tortuosity, Calcification, Stump, \\ CTO length \\ \end{tabular} & 3774 & 0.710 \\ \hline JCTO & 5 & \begin{tabular}{c} Proximal tortuosity, Calcification, \\ Stump, CTO length, Previously \\ failed lesion \\ \end{tabular} & 3760.6 & 0.708 \\ \hline RECHARGE & 5 & \begin{tabular}{c} Previous CABG, Proximal \\ tortuosity, Calcification, Stump, \\ CTO length \\ \end{tabular} & 3774.1 & 0.709 \\ \hline CL & 6 & \
\begin{tabular}{c} Previous CABG, Previous MI, \\ Calcification, Stump, CTO length, \\ n-LAD \\ \end{tabular} & 3841.7 & 0.706 \\ \hline PROGRESS & 4 &
\begin{tabular}{c} Previous PCI, Proximal tortuosity, \\ Stump, CTO artery \\ \end{tabular} & 3915.1 & 0.659 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the six models.