## 4 Results
### Experimental Result
#### 4.3.2 Comparison of Resource Scheduling Policies  
In this experiment, we assess the effectiveness of various resource scheduling policies implemented in emergency departments (EDs). The goal is to determine which strategies optimize patient flow and reduce length of stay (LoS), thereby enhancing overall departmental efficiency and patient satisfaction. We compare the LoS results of scheduling across scenarios, as shown in Table 8 detailing the simulation results of resource scheduling policies. Scenario A exhibited a range of outcomes across the scheduling strategies implemented. The integrated ML approach demonstrated superior performance, achieving the lowest LoS at 321.13 min. This underscores the effectiveness of the integrated ML strategy in optimizing operational efficiency by dynamically adapting to the complexity of patient needs, and resource availability. The Senior First Strategy using Shortest Remaining Processing Time (SRPT) also yielded favorable results with an LoS of 323.69 min, suggesting that prioritizing senior doctors can expedite treatment processes effectively. Scenario B, representing a different patient and resource dynamic, similarly showed the best outcomes with the integrated ML strategy, where the LoS was significantly reduced to 388.65 min. Notably, both the FIFO (Centroid) and the Critical Ratio (CR) with Senior First showed relatively lower LoS than other strategies, indicating their potential effectiveness in scenarios characterized by variable patient acuity and medical staffing levels. Scenario C provided a notable contrast, especially in the performance of the FIFO (Centroid) strategy, which recorded a surprisingly low LoS of 246.01 min. This outcome may reflect an optimal alignment of this strategy with the specific patient inflow and severity distribution in Scenario C. Nevertheless, the integrated ML strategy maintained consistent efficacy with the lowest LoS at 258.96 min among the more advanced strategies. The higher LoS observed in the CR (General First) and SRPT (General First) strategies suggests less efficiency in managing a diverse range of emergency cases without prioritization. This demonstrates that our proposed integrated ML approach reduces average length of stay (LoS) to approximately 322.91 min, compared to 327.10 min under traditional methods. The results indicate that the integrated ML strategy consistently outperforms traditional scheduling approaches by effectively analyzing and responding to real-time data. This approach not only minimizes LoS and waiting times but also enhances overall emergency department performance. The experiment shows the effectiveness of different scheduling strategies in three distinct emergency department scenarios across varying KTAS levels. The LoS was compared between actual data and several simulated outcomes to determine the most efficient scheduling policy. Table 9 shows the experimental results. In the analysis of various scheduling strategies across three emergency department scenarios, the integrated machine learning (ML) strategy demonstrated outstanding performance, particularly excelling in KTAS1 + 2 in Scenario A with the lowest length of stay (LoS) at 447.80 min, which shows a significant improvement over other strategies like FIFO (Random) at 456.88 min and SRPT (Senior First) at 456.32 min. This trend of superior performance by integrated ML is consistent across different KTAS levels and scenarios, suggesting its effectiveness in dynamically adapting to the complexity of patient needs. For instance, in Scenario B for KTAS1 + 2, while the FIFO (Random) strategy resulted in an LoS