## 3 Results
### Performance Measure  
The experiment was assessed in three ways. First, we evaluated the eight models configured in the DT-based prediction model. Second, we compared the proposed method with a traditional method. Finally, we measured the predictive performance of pathological T stage and BCR. The transitional method uses one machine learning model based on multiple inputs and one output. For instance, in the case of the pathology model, demography and biopsy are input data, and the output is a binary classification pathology or not. The performance of the eight ML models configured in the DT-based prediction was measured. For the experiment, a 10-fold cross validation was used as the dataset was small. The measurement performance indicator was accuracy [35]. The detailed parameters of the ML models of the comparison group were configured as shown in Table 3. Table 4 lists the experimental results for each ML method. \begin{table}
\begin{tabular}{c c} \hline \hline
**Learning Model** & **Attribute** \\ \hline ECE & Age, BMI, PSA, SVI, PI, LI, GS (sec) \\ SVI & Age, BMI, PSA PI, LI, GS (Pri), GS (Sec), Clinical T \\ PI & Age, BMI, PSA, GS (Pri), GS (Sec), Clinical T \\ LI & Age, BMI, PSA, GS (Pri), GS (Sec), Clinical T \\ SMI & Age, BMI, PSA, GS (Pri), GS (Sec), Clinical T \\ Pathology T stage & Age, BMI, PSA, EC, SVI, GS (Pri), GS (Sec) \\ PGS (SUM) & Age, BMI, PSA, SVI, PI, GS (Pri), GS (Sec), Clinical T \\ BCR & Age, BMI, PSA, ECE, LI, GS (Pri), GS (Sec), Pathology T, FGS (sum) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Learning model and attribute. Figure 7: Digital-twin-based prediction model. Among all of the algorithms, RF had the highest performance, with an average of approximately 79.89%. RNN and LSTM, which are deep learning techniques, exhibited low performance, with averages of 47.4% and 77.0%, respectively. RF was the most suitable for the dataset used in this study. Among the models, the average accuracies of SVI and BCR were the highest at 83.91% and 82.67%, respectively. The performances of the proposed and traditional methods were compared. The traditional method was trained using all of the attributes. The traditional method also used 10-fold cross validation, and the accuracy was determined. Figure 8 shows the average comparison between the DT-based prediction model and traditional method. The average accuracies of the proposed and traditional methods were 72.24% and 72.23%, respectively, and there was no significant difference. Both methods had the highest RF accuracies (79.80% and 79.89%, respectively). In other words, the algorithm did not differ significantly in the analysis results. For model comparison, the overall average of the proposed method was 78.89%, and that of the traditional method was 75.83%. Most of the models had high values for the proposed method, and SM, LVI, and PNI had the same accuracy. This was because the SM, LVI, and PNI models used the same initial data for analysis. The proposed method performed well because it used a feature selection technique that considered the PCa process. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **ECE** & **SVI** & **PNI** & **LVI** & **SM** & **Pathology T** & **GS (SUM)** & **BCR** & **Average** \\ \hline LR & 82.2 & 83.4 & 72.8 & 80.0 & 66.6 & 78.0 & 78.7 & 75.0 & 77.1 \\ NN & 77.5 & 84.2 & 68.8 & 78.0 & 62.4 & 75.5 & 79.5 & 76.7 & 75.3 \\ BN & 81.4 & 82.4 & 71.5 & 80.0 & 62.1 & 77.7 & 78.5 & 77.7 & 76.4 \\ SVM & 75.0 & 84.4 & 73.3 & 80.0 & 61.1 & 58.4 & 71.5 & 77.0 & 72.6 \\ RF & 82.2 & 85.4 & 72.8 & 83.2 & 69.1 & 80.2 & 81.7 & 84.7 & 79.9 \\ RNN & 37.4 & 38.4 & 56.7 & 59.7 & 55.4 & 50.7 & 31.4 & 49.3 & 47.4 \\ LSTM & 80.0 & 84.2 & 72.0 & 79.5 & 65.8 & 76.7 & 80.0 & 78.2 & 77.0 \\ Average & 73.7 & 77.5 & 69.7 & 77.2 & 63.2 & 71.0 & 71.6 & 74.1 & \\ \hline \end{tabular}
\end{table} 
Table 4: Accuracy of the eight ML models (%).