## 3 Results  
### Performance of ML Methods  
Table 1 shows the performance of ten ML models on the prediction of the short-term risk assessment within 90 days. Among the ten ML models, RFC was the best model achieving 89.69% accuracy, 93.83% sensitivity, 86.55% specificity, and an AUROC of 95.76%, with the testing dataset composed of 1411 positive cases and 1859 negative cases. When the train test split ratio was changed to 75:25, the GBC was the best model achieving 90.28% accuracy, 93.06% sensitivity, 88.16% specificity, and an AUROC of 95.87%, which is  
Figure 2: Heatmaps of different feature groups. (**A**) The combination of demographics information and patient medical history features heatmap; (**B**) included all patient examination results (physical examination, vital signals, laboratory results); and (**C**) the heatmap of all the CR features. slightly better than the RFC's performance (achieving 90.03% accuracy, 94.05% sensitivity, 86.98% specificity, and an AUROC of 95.77%). Only with the increasing amount of training data, GBC outperformed RCF. Without enabling the SMOTE data augmentation technique, the training accuracy was 100.00% in the RFC model training phase, and an accuracy of 89.36%, a sensitivity of 93.27%, 86.39% specificity, and an AUROC of 95.75% were obtained with the test data. Since the data imbalance is not significant, the effectiveness of SMOTE applications was not observed. Still, the RFC model with SMOTE was slightly better. As shown in Table 2, the performance of the RFC model trained only with the CR was far better than training without CR features, reflecting the importance of CR. Regarding the importance of other features, the top 10 catheterization-unrelated features of the RFC model were high-density lipids (HDL), history of angina, low-density lipids (LDL), heart rate, body mass index (BMI), diastolic blood pressure (DBP), systolic blood pressure (SBP), body surface area (BSA), weight, and serum creatinine. Table 3 shows the performance of the ten ML models on the prediction of the PCI treatment within 90 days. Among the ten ML models, GBC was the best model, achieving 92.07% accuracy, 95.50% sensitivity, 90.44% specificity, and 98.02% as an AUROC, with the testing dataset composed of 867 positive cases and 1820 negative cases. When the train test split ratio was changed to 75:25, the GBC was still the best model achieving 92.26% accuracy, 94.47% sensitivity, 91.21% specificity, and an AUROC of 97.95%. The SMOTE method mitigated the data imbalance issue, and the GBC model's performance was enhanced. As shown in Table 4, the performance of the GBC model trained only with the CR was also found to be better than training without CR features, which revealed the importance of CR. Other than catheterization-related features, history of angina, acute coronary syndrome (ACS), HDL, LDL, GFR Stage, DBP, BSA, BMI, age, and history of diabetes were among the top ten important features in the GBC model for predicting the PCI treatment within 90 days. \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUROC (\%)** \\ \hline LDA & 88.13 & 92.20 & 85.05 & 93.67 \\ KNC 1  & 83.03 & 77.53 & 87.20 & 89.53 \\ DTC & 83.58 & 82.00 & 84.78 & 83.39 \\ GNB & 75.90 & 95.68 & 60.89 & 89.15 \\ SVC & 87.31 & 91.35 & 84.24 & 93.87 \\ LR & 88.47 & 89.58 & 87.63 & 93.57 \\ RFC & 89.69 & 93.83 & 86.55 & 95.76 \\ GBC & 89.05 & 91.85 & 86.93 & 95.58 \\ ABC & 88.53 & 89.09 & 88.11 & 94.41 \\ MLP 2  & 87.61 & 87.53 & 87.68 & 94.26 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance of ML models in predicting cardiovascular events and treatments, including CABG, PCI, VS, stroke, and MI. \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUROC (\%)** \\ \hline No-CR & 67.89 & 59.32 & 74.39 & 73.97 \\ CR & 88.90 & 91.92 & 86.61 & 95.37 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance of the RFC model trained without CR and trained only with CR.