## Abbreviations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**RF**} & \multicolumn{1}{c}{**ANN**} & \multicolumn{1}{c}{**LR**} & \multicolumn{1}{c}{**SVM**} & \multicolumn{1}{c}{**LGBM**} & \multicolumn{1}{c}{**CB**} & \multicolumn{1}{c}{**XGB**} \\ \hline RF & — & \(-0.614/(0.541)\) & **5.992/(0.000)** & \(-1.228/(0.223)\) & \(-0.104/(0.917)\) & \(-1.673/(0.098)\) & \(-1.426/(0.157)\) \\ \hline ANN & \(0.614/(0.541)\) & — & **6.104/(0.000)** & \(-0.190/(0.850)\) & \(0.534/(0.594)\) & \(-1.034/(0.304)\) & \(-0.881/(0.380)\) \\ \hline LR & \(\mathbf{-5.992/(0.000)}\) & \(\mathbf{-6.104/(0.000)}\) & — & \(\mathbf{-6.301/(0.000)}\) & \(\mathbf{-5.718/(0.000)}\) & \(\mathbf{-6.356/(0.000)}\) & \(\mathbf{-6.198/(0.000)}\) \\ \hline SVM & \(1.228/(0.223)\) & \(0.190/(0.850)\) & \(\mathbf{6.301/(0.000)}\) & — & \(0.914/(0.363)\) & \(-1.024/(0.309)\) & \(-0.887/(0.377)\) \\ \hline LGBM & \(0.104/(0.917)\) & \(-0.534/(0.594)\) & \(\mathbf{5.718/(0.000)}\) & \(-0.914/(0.363)\) & — & \(-1.550/(0.124)\) & \(-1.322/(0.189)\) \\ \hline CB & \(1.673/(0.098)\) & \(1.034/(0.304)\) & \(\mathbf{6.356/(0.000)}\) & \(1.024/(0.309)\) & \(1.550/(0.124)\) & — & \(0.078/(0.938)\) \\ \hline XGB & \(1.426/(0.157)\) & \(0.881/(0.380)\) & \(\mathbf{6.198/(0.000)}\) & \(0.887/(0.377)\) & \(1.322/(0.189)\) & \(-0.078/(0.938)\) & — \\ \hline
**Precision** & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & — & \(\mathbf{-2.973/(0.004)}\) & \(\mathbf{2.269/(0.025)}\) & \(-0.294/(0.769)\) & \(-1.276/(0.205)\) & \(\mathbf{-3.692/(0.000)}\) & \(\mathbf{-3.365/(0.001)}\) \\ \hline ANN & \(\mathbf{2.973/(0.004)}\) & — & \(\mathbf{3.915/(0.000)}\) & \(\mathbf{2.947/(0.004)}\) & \(\mathbf{2.151/(0.034)}\) & \(-0.076/(0.940)\) & \(-0.212/(0.833)\) \\ \hline LR & \(\mathbf{-2.269/(0.025)}\) & \(\mathbf{-3.915/(0.000)}\) & — & \(\mathbf{-2.447/(0.016)}\) & \(\mathbf{-2.647/(0.009)}\) & \(\mathbf{-3.937/(0.000)}\) & \(\mathbf{-4.017/(0.000)}\) \\ \hline SVM & \(0.294/(0.769)\) & \(\mathbf{-2.947/(0.004)}\) & \(\mathbf{2.447/(0.016)}\) & — & \(-0.897/(0.372)\) & \(\mathbf{-4.353/(0.000)}\) & \(\mathbf{-4.114/(0.000)}\) \\ \hline LGBM & \(1.276/(0.205)\) & \(\mathbf{-2.151/(0.034)}\) & \(\mathbf{2.647/(0.009)}\) & \(0.897/(0.372)\) & — & \(\mathbf{-2.657/(0.009)}\) & \(\mathbf{-2.596/(0.011)}\) \\ \hline CB & \(\mathbf{3.692/(0.000)}\) & \(0.076/(0.940)\) & \(\mathbf{3.937/(0.000)}\) & \(\mathbf{4.353/(0.000)}\) & \(\mathbf{2.657/(0.009)}\) & — & \(-0.234/(0.816)\) \\ \hline XGB & \(\mathbf{3.365/(0.001)}\) & \(0.212/(0.833)\) & \(\mathbf{4.017/(0.000)}\) & \(\mathbf{4.114/(0.000)}\) & \(\mathbf{2.596/(0.011)}\) & \(0.234/(0.816)\) & — \\ \hline
**Recall** & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & — & \(\mathbf{2.243/(0.027)}\) & \(\mathbf{9.097/(0.000)}\) & \(-1.382/(0.170)\) & \(1.155/(0.251)\) & \(1.761/(0.081)\) & \(2.015/(0.047)\) \\ \hline ANN & \(\mathbf{-2.243/(0.027)}\) & — & \(4.769/(0.000)\) & \(\mathbf{-3.164/(0.002)}\) & \(-1.355/(0.178)\) & \(-1.085/(0.281)\) & \(-0.790/(0.432)\) \\ \hline LR & \(\mathbf{-9.097/(0.000)}\) & \(\mathbf{-4.769/(0.000)}\) & — & \(\mathbf{-8.685/(0.000)}\) & \(\mathbf{-8.008/(0.000)}\) & \(\mathbf{-6.643/(0.000)}\) & \(\mathbf{-6.268/(0.000)}\) \\ \hline SVM & \(1.382/(0.170)\) & \(\mathbf{3.164/(0.002)}\) & \(\mathbf{8.685/(0.000)}\) & — & \(\mathbf{2.204/(0.030)}\) & \(\mathbf{3.664/(0.000)}\) & \(\mathbf{3.674/(0.000)}\) \\ \hline LGBM & \(-1.155/(0.251)\) & \(1.355/(0.178)\) & \(\mathbf{8.008/(0.000)}\) & \(\mathbf{-2.204/(0.030)}\) & — & \(0.612/(0.542)\) & \(0.847/(0.399)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Corrected 10 xRepeated 10-fold-CV paired \(t\)-test results across multiple metrics (T-score/\(p\)-value).