## 4 Results
### Machine Learning Model Performance  
First, we generated simulation result data. For training, we obtained data by applying 6 scheduling algorithms to a total of 9 scenarios. Out of a total of 768 data generated from the simulation, we selected a final dataset of 192 with the lowest waiting times, which represents the top 25%. Some of the input and output data used for training are shown in Table 6.  
Next, we proceed with training using a classification model. The algorithm employed is Backpropagation of the Neural Network (NN). The input layer consists of 7 nodes, with 1 hidden node and 1 output node. The training was executed 200 times, utilizing the sigmoid function as the activation function. For comparison of machine learning algorithms, we used Logistic Regression, C 5.0, and Naive Bayesian. Machine learning performance evaluation was conducted using a 3-fold method on 192 data. The experimental results for the 4 machine learning algorithms and 192 data are shown in Figure 4. Performance evaluation was based on Sensitivity, Specificity, and Accuracy.  
Sensitivity, Specificity, and Accuracy values recorded were above 93. While differences among experimental groups were not significant, the Neural Network exhibited the highest accuracy at 93.98%. The reason for insignificant differences among algorithms can be interpreted as most of the training data being biased toward specific scheduling policies, leading to the predominant usage of scheduling policies with superior performance. The most frequently used policy was CR (Senior First), followed by SRPT (General First) and CR (General First). In contrast, policies FIFO (Random), FIFO (Centroid), and SRPT (Senior First) were rarely used. Thus, it can be inferred that the performance of specific scheduling policies is better than using all policies.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Record**} & \multicolumn{3}{c}{**Queue**} & \multicolumn{3}{c}{**Doctor**} & \multirow{2}{*}{
3 & 1 & 2 & 2 & 2 & 1 & 5 & 5 & 3 \\  & & & & & & & & \\
192 & 2 & 2 & 2 & 1 & 3 & 4 & 6 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Learning dataset (simulation output data).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{\begin{tabular}{c} **KTAS and** \\ **Discharge** \\ \end{tabular} } & \multicolumn{6}{c}{**Patient Arrival (Number of Patient)**} & \multicolumn{6}{c}{**Resource**} \\ \cline{3-14}  & & **Total** & **0–3** & **3–6** & **6–9** & **9–12** & **12–15** & **15–18** & **18–21** & **21–24** & \
\begin{tabular}{c} **General** \\ **Doctor** \\ \end{tabular} &
\begin{tabular}{c} **Senior** \\ **Doctor** \\ \end{tabular} & **Nurse** \\ \hline \multirow{4}{*}{A} & Total & 105 & 10 & 6 & 9 & 17 & 16 & 16 & 15 & \multirow{4}{*}{7} & \multirow{4}{*}{6} & \multirow{4}{*}{32} \\  & KTAS 1, 2 & 8 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & & \\  & KTAS 3 & 45 & 4 & 3 & 4 & 8 & 8 & 7 & 6 & 6 & 6 & \\  & KTAS 4, 5 & 52 & 5 & 3 & 4 & 7 & 7 & 7 & 9 & 9 & & \\ \hline \multirow{4}{*}{B} & Total & 99 & 8 & 5 & 8 & 17 & 16 & 16 & 15 & 14 & \multirow{4}{*}{5} & \multirow{4}{*}{4} & \multirow{4}{*}{23} \\  & KTAS 1, 2 & 11 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 1 & \\  & KTAS 3 & 60 & 4 & 3 & 5 & 11 & 11 & 10 & 9 & 7 & 5 & 4 & 23 \\  & KTAS 4, 5 & 28 & 3 & 2 & 2 & 4 & 3 & 4 & 5 & 5 & & \\ \hline \multirow{4}{*}{C} & Total & 97 & 9 & 6 & 8 & 15 & 13 & 14 & 16 & 15 & \multirow{4}{*}{7} & \multirow{4}{*}{6} & \multirow{4}{*}{23} \\  & KTAS 1, 2 & 7 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & \\ \cline{1-1}  & KTAS 3 & 41 & 4 & 3 & 4 & 7 & 6 & 6 & 6 & 6 & 6 & \\ \cline{1-1}  & KTAS 4, 5 & 49 & 5 & 3 & 4 & 7 & 6 & 7 & 9 & 9 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Patient arrival data for the three regions of NEDIS (calculated for one day).