# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 5 Discussion: Limitations and Future Work
This approach is particularly valuable in international contexts where resource distribution and infrastructure conditions vary widely. Another important extension of this work involves incorporating hospital-level variablessuch as bed availability, emergency department capacity, and post-transport treatment times. These factors significantly influence EMS system efficiency and patient outcomes but were beyond the scope of the present study. Future research should integrate such downstream variables to more comprehensively model the full emergency care pathway. Furthermore, although Random Forest offers built-in measures of feature importance, it does not inherently support fine-grained interpretability or localized decision analysis. To address this limitation, future studies should consider incorporating advanced explanation tools such as SHAP (Shapley Additive Explanations), which can provide both global and local interpretability by quantifying the contribution of each feature to individual predictions. By visualizing SHAP summary plots, researchers and EMS administrators can gain deeper insights into the model's decision-making logic, enabling more transparent and trustable deployment strategies. Incorporating such methods would enhance the interpretability and accountability of machine learning applications in high-stakes, policy-sensitive environments such as emergency medical services. While interpretability methods such as SHAP can reveal the contribution of individual variables at both global and local levels, another important dimension of analytical rigor lies in assessing the statistical significance of model performance differences [38]. In the present study, although the comparative performance of candidate models (e.g., Random Forest, LightGBM, XGBoost) was evaluated using standard metrics such as MAE and RMSLE, no formal statistical hypothesis testing--such as paired \(t\)-tests or repeated measures ANOVA--was conducted. This omission limits our ability to determine whether the observed performance gaps are truly meaningful or potentially due to random variance within the data or the resampling process. The decision not to include inferential statistical tests in this version was influenced by several factors. First, the EMS data used in this study are highly imbalanced and operationally stratified across time and geography, posing challenges to standard test assumptions such as normality, homoscedasticity, and independent sampling. Second, developing a valid experimental design that supports robust significance testing would require additional bootstrapping or cross-validation configurations, which were beyond the current project scope. Nevertheless, future work should explicitly incorporate statistical significance testing to validate model selection outcomes and enhance the credibility of the findings--especially when used to inform public resource allocation in sensitive domains such as emergency medical services. Combining model interpretability (via SHAP) with statistical validity (via hypothesis testing) will allow researchers and decision-makers to better understand both why a model makes certain predictions and whether it is demonstrably superior in performance, thus advancing both transparency and accountability in data-driven EMS planning. To address potential concerns regarding the operational safety of data-driven ambulance allocation, it is important to clarify that the proposed framework does not prescribe fixed or absolute deployment levels. Instead, the model generates recommendations based on comparative analysis against a benchmark cohort of 100 EMS centers that were empirically designated by the National Fire Agency as having achieved balanced performance across key operational indicators, including responsiveness, efficiency, and workload stability. These centers serve as a reference baseline for what constitutes an empirically \"adequate\" level of ambulance deployment. Moreover, the simulation module provides a flexible environment for scenario-based policy evaluation.