## 3 Results
### Results of Machine Learning Model
#### 3.2.2 Regression Model  
The performance of the regression model in predicting the change in CTSI-JSSH-SSS scores before and after surgery is shown in Table 5. The RF model showed the best RMSE and R\({}^{2}\) score among the five models. Visualizing the feature importance of the RF model, preoperative CTSI-JSSH-SS score, preoperative CTSI-JSSH-FS score, and age were important factors. Similarly, preoperative CTSI-JSSH-SS score, preoperative CTSI-JSSH-FS score, and preoperative DML were important factors for the SHAP score of the RF model (Figure 4). The performance of the model in predicting changes in CTSI-JSSH-FS before and after surgery is shown in Table 6. The RF model had the best RSME and R\({}^{2}\) scores of the five models. Visualization of feature importance and SHAP values showed that preoperative CTSI-JSSH-FS score, age, and preoperative CTSI-SS score were important factors (Figure 5). Figure 3: (a) Feature importance; (b) SHAP scores of the LGBM model in predicting electrophysiological severity one year after the surgery. Figure 2: The AUC obtained from the ROC curve was 0.754 (0.752–0.757) for LR, 0.765 (0.763–0.768) for SVM, 0.764 (0.761–0.766) for RF, and 0.802 (0.799–0.804) for LGBM. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **LR** & **EN** & **SVM** & **RF** & **LGBM** \\ \hline RMSE & 0.405 & 0.404 & 0.399 & 0.333 & 0.375 \\  & (0.403–0.407) & (0.402–0.406) & (0.397–0.401) & (0.332–0.335) & (0.373–0.377) \\ \hline MAE & 0.289 & 0.288 & 0.271 & 0.238 & 0.234 \\  & (0.287–0.290) & (0.287–0.289) & (0.270–0.273) & (0.236–0.239) & (0.233–0.235) \\ \hline R\({}^{2}\) & 0.771 & 0.773 & 0.779 & 0.846 & 0.803 \\  & (0.769–0.774) & (0.771–0.776) & (0.776–0.781) & (0.844–0.847) & (0.801–0.806) \\ \hline Best Parameters of Model & Model intercept = 1.22 & Alpha = 0.0021 & \multirow{2}{*}{C = 0.464 \(\text{ratio}=0.00\)} & Max depth = 5 \(\text{Gamma}=0.022\) & Objective = \\  & & & & & Mean absolute error \\ \hline \end{tabular}
\end{table} 
Table 6: Model performance in the task of detecting the change in CTSI-FS scores. Figure 4: **(a)** Feature importance, **(b)** SHAP scores of the RF model in predicting changes in CTSI-JSSH-SS scores one year after the surgery. \begin{table} \begin{tabular}{c c c c c c} \hline  & **LR** & **EN** & **SVM** & **RF** & **LGBM** \\ \hline RMSE & 0.486 & 0.485 & 0.487 & 0.418 & 0.475 \\  & (0.478–0.494) & (0.476–0.494) & (0.478–0.495) & (0.410–0.426) & (0.464–0.485) \\ \hline MAE & 0.337 & 0.336 & 0.315 & 0.285 & 0.267 \\  & (0.332–0.342) & (0.330–0.341) & (0.309–0.320) & (0.280–0.290) & (0.261–0.272) \\ \hline R2 & 0.688 & 0.685 & 0.683 & 0.768 & 0.702 \\  & (0.679–0.697) & (0.675–0.695) & (0.672–0.694) & (0.760–0.776) & (0.691–0.713) \\ \hline Best Parameters of Model & Model intercept = 1.39 & Alpha = 0.004 & C = 0.464 \(\text{Gamma}=0.004\) & Max depth = 5 \(\text{Gamma}=0.022\) & Learning \\  & & & & & \\ \hline \end{tabular} \begin{tabular}{c c c c c} \hline  & **LR** & **EN** & **SVM** & **RF** & **LGBM** \\ \hline RMSE & 0.405 & 0.404 & 0.399 & 0.333 & 0.375 \\  & (0.403–0.407) & (0.402–0.406) & (0.397–0.401) & (0.332–0.335) & (0.373–0.377) \\ \hline MAE & 0.289 & 0.288 & 0.271 & 0.238 & 0.234 \\  & (0.287–0.290) & (0.287–0.289) & (0.270–0.273) & (0.236–0.239) & (0.233–0.235) \\ \hline R\({}^{2}\) & 0.771 & 0.773 & 0.779 & 0.846 & 0.803 \\  & (0.769–0.774) & (0.771–0.776) & (0.776–0.781) & (0.844–0.847) & (0.801–0.806) \\ \hline Best Parameters of Model & Model intercept = 1.22 & Alpha = 0.0021 \(\text{Gamma}=0.0021\) & \multirow{2}{*}{C = 0.464 \(\text{Gamma}=0.022\)} & Max depth = 5 \(\text{Gamma}=0.022\) & Objective = \\  & & & & & Mean absolute error \\ \hline \end{tabular}