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Application of machine learning techniques for ambulance coverage prediction | IEEE Conference Publication | IEEE Xplore

Application of machine learning techniques for ambulance coverage prediction


Abstract:

This study contributes to the improvement of ambulance deployment strategies and the overall enhancement of emergency response systems. Ambulance coverage, a vital metric...Show More

Abstract:

This study contributes to the improvement of ambulance deployment strategies and the overall enhancement of emergency response systems. Ambulance coverage, a vital metric for evaluating the effectiveness of emergency medical services (EMS), is investigated in this research. Various ensemble learning models are examined to identify the optimal approach to predict ambulance coverage rate. CatBoost demonstrates exceptional predictive precision, with an adjusted R-squared value of 91.7%, a Root Mean Square Error of 0.0192, and a computation time of 37.48 seconds.
Date of Conference: 02-04 May 2024
Date Added to IEEE Xplore: 28 June 2024
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ISSN Information:

Conference Location: Sousse, Tunisia

References

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