# Prediction System for Prostate Cancer Recurrence Using Machine Learning
## 3 Results
### Development of BCR Prediction System  
The BCR prediction system used data from Korean patients with prostate cancer to implement a web-based application. The structure of the prediction system was classified into two parts: predictive modeling and web application development (Figure 3). The web application used the predictive model to make predictions.  
The BCR prediction system was set up to perform several functions on one screen, including reading the data list of patients with prostate cancer, inputting the input values, and extracting the prediction results (Figure 4). After the input fields, including age at diagnosis, initial PSA, clinical T stage, pathological Gleason score, and pathological T stage values have been entered and status information such as surgical margin, perineural invasion, seminal vesicle invasion, extracapsular extension, and lymphovascular invasion has been selected, the three and five year post-surgery BCR predictions could be extracted. The prediction results appear as an output item at the bottom of the screen.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Analysis**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**AUC**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Sensitivity**} & \multicolumn{2}{c}{**Specificity**} & \multicolumn{2}{c}{**MCC**} \\ \cline{3-11}  & & **3-year** & **5-year** & **3-year** & **5-year** & **3-year** & **5-year** & **3-year** & **5-year** & **3-year** & **5-year** \\ \hline \multirow{4}{*}{Trees} & Decision Tree & 0.8186 & 0.7561 & 0.7817 & 0.7033 & 0.4135 & 0.7009 & 0.9447 & 0.7062 & 0.4448 & 0.4055 \\  & Random Forest (ntrees = 20) \({}^{1}\) & 0.8320 & 0.7956 & 0.7817 & 0.7126 & 0.4135 & 0.7308 & 0.9447 & 0.6907 & 0.4448 & 0.4210 \\  & Random Forest (ntrees = 50) & 0.8349 & 0.8047 & 0.7832 & 0.7266 & 0.4279 & 0.7393 & 0.9404 & 0.7113 & 0.4495 & 0.4310 \\  & Random Forest (ntrees = 80) & 0.8362 & 0.8050 & 0.7876 & 0.7196 & 0.4327 & 0.7308 & 0.9447 & 0.7062 & 0.4621 & 0.4360 \\ \hline \multirow{4}{*}{Neural Networks} & 1 hidden, dropout = 0.3 at input & 0.7939 & 0.7895 & 0.7611 & 0.7056 & 0.3606 & 0.7821 & 0.9383 & 0.6134 & 0.4885 & 0.4001 \\  & 1 hidden dropout = 0.1 at input & 0.8027 & 0.7977 & 0.7699 & 0.6916 & 0.5144 & 0.7393 & 0.8830 & 0.6340 & 0.4089 & \(-0.0367\) \\  & 2 hidden dropout = 0.3 at input & 0.7978 & 0.7941 & 0.6932 & 0.7056 & 0.0000 & 0.7051 & 1.0000 & 0.7062 & 0.3393 & 0.3986 \\  & 2 hidden dropout = 0.1 at input & 0.7988 & 0.7967 & 0.7611 & 0.7056 & 0.3702 & 0.8162 & 0.9340 & 0.5722 & 0.3963 & 0.4128 \\ \hline Survival Regression & Cox PH & 0.7944 & 0.7816 & 0.8025 & 0.7830 & 0.2160 & 0.3333 & 0.9568 & 0.9300 & 0.2894 & 0.3159 \\ \hline Logistic & Ridgeed Regression (L2) & 0.8288 & 0.8071 & 0.7802 & 0.7290 & 0.4279 & 0.7265 & 0.9362 & 0.7320 & 0.4413 & 0.4210 \\  & Lasso (L1) & 0.8319 & 0.7993 & 0.7861 & 0.7243 & 0.4519 & 0.7179 & 0.9340 & 0.7320 & 0.4590 & 0.4481 \\  & GBC & 0.8419 & 0.8031 & 0.7891 & 0.7407 & 0.4567 & 0.7436 & 0.9362 & 0.7371 & 0.4348 & 0.4836 \\ \hline \hline \end{tabular}  
* \({}^{1}\) \"ntrees\" means the number of trees in the forest.  
\end{table}
Table 2: Statistical Method Analysis.  
Figure 3: System Flow.