## 3 Results
### Performance of ML Methods
Table 5 shows the performance of the ML models on the prediction of CABG treatment within 90 days. Among the ten ML models, GBC was the best model, achieving 95.45% accuracy, 86.31% sensitivity, 96.79% specificity, and 98.33% as an AUROC, with the testing dataset composed of 263 positive cases and 1805 negative cases. When the train test split ratio was changed to 75:25, the GBC was still the best model achieving 95.65% accuracy, 83.33% sensitivity, 97.45% specificity, and an AUROC of 98.40%. Compared with two previous cases, this data was significantly imbalanced, and the number of CABG samples was far less than the negative group, which directly lowered the sensitivity even under engaging the SMOTE. As shown in Table 6, the performance of the GBC model trained only with the CR was found to be better than training without CR features, as in the other two predictions. Aside from the catheterization-related features, history of angina, age, race, heart rate, height, serum creatinine, HDL, DBP, SBP, and LDL were the top ten most important features in the descending order. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUROC (\%)** \\ \hline No CR & 86.65 & 20.15 & 96.34 & 76.12 \\ CR & 94.49 & 85.17 & 95.84 & 97.73 \\ \hline \hline \end{tabular}  
* KNC model with setting the weights parameter as ‘distance’ was better than its default. 2 with a hidden layer of 38 dimensions was the best MLP model among the model selection.  
\end{table} 
Table 6: Performance of the GBC model trained without CR and trained only with CR. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUROC (\%)** \\ \hline LDA & 89.36 & 94.00 & 87.14 & 96.19 \\ KNC 1 & 85.34 & 73.24 & 91.10 & 91.15 \\ DTC & 87.12 & 81.78 & 89.67 & 85.72 \\ GNB & 36.32 & 97.81 & 7.03 & 53.86 \\ SVC & 89.21 & 88.00 & 89.78 & 95.69 \\ LR & 89.62 & 87.20 & 90.77 & 95.76 \\ RFC & 91.40 & 95.62 & 89.40 & 97.46 \\ GBC & 92.07 & 95.50 & 90.44 & 98.02 \\ ABC & 90.36 & 89.39 & 90.82 & 96.91 \\ MLP 2 & 89.58 & 86.62 & 90.99 & 96.10 \\ \hline \hline \end{tabular}  
* KNC model with setting the weights parameter as ‘distance’ was better than its default. 2 with a hidden layer of 38 dimensions was the best MLP model among the model selection.  
\end{table} 
Table 3: Performance of ML models in the prediction of receiving PCI treatments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUROC (\%)** \\ \hline No CR & 72.57 & 47.87 & 84.34 & 76.66 \\ CR & 91.63 & 95.62 & 89.73 & 97.93 \\ \hline \hline \end{tabular}  
\end{table} 
Table 4: Performance of the GBC model trained without CR and trained only with CR. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUROC (\%)** \\ \hline LDA & 89.36 & 94.00 & 87.14 & 96.19 \\ KNC 1 & 85.34 & 73.24 & 91.10 & 91.15 \\ DTC & 87.12 & 81.78 & 89.67 & 85.72 \\ GNB & 36.32 & 97.81 & 7.03 & 53.86 \\ SVC & 89.21 & 88.00 & 89.78 & 95.69 \\ LR & 89.62 & 87.20 & 90.77 & 95.76 \\ RFC & 91.40 & 95.62 & 89.40 & 97.46 \\ GBC & 92.07 & 95.50 & 90.44 & 98.02 \\ ABC & 90.36 & 89.39 & 90.82 & 96.91 \\ MLP 2 & 89.58 & 86.62 & 90.99 & 96.10 \\ \hline \hline \end{tabular}  
* KNC model with setting the weights parameter as ‘distance’ was better than its default. 2 with a hidden layer of 38 dimensions was the best MLP model among the model selection.  
\end{table} 
Table 3: Performance of ML models in the prediction of receiving PCI treatments. In Figure 2A, based on the color of the heatmaps, age, and HXANGINA (history of angina) were the most important features within the demographic information and patient medical history for all three prediction cases.