## 4 Discussion  
AI has been used to build models in the field of urology in recent years. Several studies have reported the effectiveness of applying AI to PCa. We have proposed an NN-based BCR prediction model and pruned the model to remove unnecessary node connections in the NN. The PCNN was constructed by performing feature-sensitivity analysis and partial correlation analysis for optimized BCR prediction. This study is the first to optimize an NN model for developing a BCR-prediction model. The PCNN achieved good performance (accuracy: 87.16%) in BCR prediction. XGBoost had a higher sensitivity than the PCNN but a lower accuracy and specificity. Overall, the PCNN achieved higher performance, and the value of the specificity consisting of a small number of classes for predicting BCR was higher. Moreover, the PCNN is more effective than XGBoost because it consumes a lower cost and time. The tree algorithms (XGBoost (Tree) and RF) are generally suitable for these data because more categorical variables results in better performance. Furthermore, the performance was high when the boosting technique was used. Deep learning models generally achieve high performance when they have numerous data and features. Therefore, the performance of RNN and LSTM was low in the data used in this study. The PCNN analyzes the partial correlation of input variables and configures the most suitable NN architecture for BCR prediction; thus, the experimental performance was high. The proposed PCNN improved the performance by approximately 4% compared to other algorithms and is suitable for BCR prediction. The performance is expected to be higher for large amounts of data. [PERSON] [9] predicted the accuracy of BCR using Cox regression, RF, KNN, and LR after robot-assisted prostatectomy. When using the same control RF, the same accuracy was \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Testing**} \\ \hline  & **Accuracy** & **Sensitivity** & **Specificity** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline SVM & 89.16 & 94.34 & 86.26 & 71.12 & 74.56 & 68.20 \\ BN & 88.86 & 92.64 & 88.80 & 70.96 & 73.77 & 70.47 \\ RF & 94.60 & 100 & 92.28 & 72.05 & 76.25 & 70.36 \\ XGBoost & & & & & & \\ (LR) & 94.24 & 96.40 & 91.88 & 71.63 & 73.67 & 70.57 \\ XGBoost & & & & & & \\ (Tree) & 94.60 & 100 & 92.28 & 72.14 & 76.23 & 70.85 \\ NN & 87.84 & 92.64 & 86.26 & 69.79 & 73.54 & 68.85 \\ RNN & 86.64 & 92.22 & 84.42 & 67.73 & 72.26 & 67.78 \\ LSTM & 88.86 & 96.40 & 86.26 & 71.12 & 72.26 & 68.85 \\ PCNN & 93.60 & 97.60 & 92.28 & 74.70 & 77.57 & 72.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance measures (training, testing = 66:33). SVM, support vector machine; BN, Bayesian network; RF, random forest; NN, neural network; RNN, recurrent neural network; LSTM, long short-term memory; PCNN, partial correlation neural network. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline SVM & 83.29 & 87.82 & 81.00 \\ BN & 83.52 & 86.57 & 82.88 \\ RF & 86.66 & 91.89 & 84.24 \\ XGBoost (LR) & 86.16 & 88.42 & 84.85 \\ XGBoost (Tree) & 86.66 & 91.43 & 84.94 \\ NN & 82.18 & 86.77 & 80.59 \\ RNN & 81.68 & 84.64 & 80.44 \\ LSTM & 84.78 & 88.42 & 83.34 \\ PCNN & 87.16 & 90.80 & 85.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance measures (10-fold cross-validation). SVM, support vector machine; BN, Bayesian network; RF, random forest; NN, neural network; RNN, recurrent neural network; LSTM, long short-term memory; PCNN, partial correlation neural network. achieved at 86.6%. Thus, the data used in this study can be considered as significant for predicting BCR. [PERSON] [37] predicted the accuracy using LR, SVM, and XGBoost to predict whether death due to PCa occurred. When the same XGBoost was used in the study of [PERSON] [37] and our study, the performance was 94.68% and 86.16%, respectively, but the results may differ depending on the number of samples in the dataset used.