A Stacking Ensemble Machine Learning Model for Emergency Call Forecasting
Bibliographic record
Abstract
One of the greatest challenges of Emergency medical services providers is to handle the large number of Emergency Medical Service (EMS) calls coming from the population. An accurate forecast of EMS calls is involved in ambulance fleet dispatching and routing to minimize response times to emergency calls and enhance the efficacy of assistance. Yet, the demand for emergency services exhibits significant variability, posing a challenge in accurately predicting the future occurrence of emergency calls and their spatial-temporal distribution. Here, we propose a stacking ensemble machine learning model to forecast EMS calls, combining different base learners to enhance the overall performance of generalization. Additionally, we conducted experiments using Boruta, Lasso, RFFI and SHAP feature selection methods to identify the most informative attributes from the EMS dataset. The proposed ensemble model integrates a base layer and a meta layer. In the base layer, we applied four base learners: Decision Tree, Gradient Boosting Regression Tree, Light Gradient Boosting Machine and Random Forest. In the meta layer, we used an optimized Random Forest model to integrate the outputs of base learners. We evaluate the performance of our proposed model using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>-score and four different error metrics. Based on a real data set including spatial, temporal and weather features, the findings of this study demonstrated that the proposed stacking-based ensemble model showed a better score and the minimum errors compared to the traditional single algorithms, online machine learning methods and voting ensemble methods. We achieved a higher score of 0.9954, mse of 0.8938, rmse of 0.9454, mae of 0.2923 and mape of 0.0724 compared to state-of-the-art models. This work is an aid for emergency managers in making well-informed decisions, improving outcomes for ambulance dispatch and routing, and enhancing ambulance response time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".