Application of machine learning techniques for ambulance coverage prediction

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.