## 4 Results
### Feature Importance  
Different algorithms have different calculations. Table 2 presents the ranking of the selected variables by the six machine learning methods. LGR selected a total of 32 variables, 6 by CART, 10 by MARS, 26 by RF, and 13 by XGboost. A larger value means higher importance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**LGR**} & \multicolumn{1}{c}{**CART**} & \multicolumn{1}{c}{**MARS**} & \multicolumn{1}{c}{**RF**} & \multicolumn{1}{c}{**XGboost**} & \multicolumn{1}{c}{**Average**} \\  & \multicolumn{1}{c}{**(N = 32)**} & \multicolumn{1}{c}{**(N = 6)**} & \multicolumn{1}{c}{**(N = 10)**} & \multicolumn{1}{c}{**(N = 26)**} & \multicolumn{1}{c}{**(N = 13)**} & \\ \hline \multicolumn{10}{c}{**Baseline**} \\ \hline X1 & Sex & 4 & - & - & 15 & - & 3.8 \\ X2 & Age & 32 & 31 & 32 & 32 & 32 & 31.8 \\ X3 & CCI scores & - & 30 & 29 & 29 & 29 & 24.2 \\ \hline \multicolumn{10}{c}{One year before variables} \\ \hline X4 & Previous CABG surgery & 3 & - & - & - & - & 0.6 \\ X5 & Length of ICU stay (Days) & 8 & - & - & 8 & - & 3.2 \\ X6 & Blood transfusion (Bags) & 11 & - & - & 23 & - & 6.8 \\ X7 & Mechanical ventilation (Days) & - & - & - & 16 & - & 3.2 \\ \hline \multicolumn{10}{c}{Current surgical variables} \\ \hline X8 & Blood transfusion (Bags) & 12 & 28 & 24 & 29 & 28 & 24.2 \\ X9 & Mechanical ventilation (Days) & 16 & - & - & 25 & 24 & 13 \\ \hline \multicolumn{10}{c}{**Comorbidities**} \\ \hline X12 & Hypertension & 14 & - & - & - & - & 2.8 \\ X13 & Hyperlipidemia & 28 & - & 27 & 26 & 26 & 21.4 \\ X14 & Hyperuricemia & 23 & - & - & - & - & 4.6 \\ X15 & Diabetes & 29 & - & 26 & 17 & 25 & 19.4 \\ X17 & COPD & 20 & - & - & 21 & - & 8.2 \\ X18 & Skin bone & 2 & - & - & - & - & 0.4 \\ X19 & Stroke & 10 & - & - & 18 & - & 5.6 \\ X20 & Gout & - & - & - & 7 & - & 1.4 \\ X22 & HBV & 19 & - & - & - & - & 3.8 \\ X23 & HCV & 18 & - & - & - & - & 3.6 \\ \hline \multicolumn{10}{c}{Heart-related characteristics} \\ \hline X24 & AF & 15 & - & - & 10 & - & 5 \\ X25 & CHF & 31 & 29 & 30 & 30 & 30 & 30 \\ X26 & PVD & 6 & - & - & - & - & 1.2 \\ X27 & ACS & 22 & - & 25 & 22 & 23 & 18.4 \\ X28 & Malignant dysrhythmia & 27 & - & 23 & 11 & 20 & 16.2 \\ X29 & Cardiogenic shock & 25 & - & 28 & 12 & 22 & 17.6 \\ \hline \multicolumn{10}{c}{Real-related characteristics} \\ \hline X30 & KD & 7 & 32 & - & 28 & 27 & 18.8 \\ X31 & CRF & 30 & 27 & 31 & 27 & 29 & 28.8 \\ X32 & AKF & 26 & - & - & 20 & - & 9.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ranking of the selected variable by the six machine learning methods. Figure 2 presents the top 15 important risk factors ranked corresponding to the six machine learning models. The rankings of each selected variable in the six prediction models were as follows: age (which contributed the most to this model), CHF, CRF, CCI scores, hyperlipidemia, diabetes, KD, ACS, cardiogenic shock, malignant dyshythmia, average days on a ventilator during recent surgery, gastrointestinal bleeding, AKF, and COPD. These 15 features can be considered the main variables for the prediction model. These rankings can give physicians an overview of the patient before or after the surgery. Furthermore, these variables were used to develop the ensemble learning prediction model. Figure 3 shows the decision rules for predicting mortality among patients who underwent 3-vessel PCI for the first time, based on the three important features of the CART