1- Department of Management of Statistics and Information Technology, Golestan University of Medical Sciences, Gorgan, Iran
2- Department of Statistics, Faculty of Mathematical Sciences, University of Mazandaran, Babolsar, Iran ,m.babanezhad@umz.ac.ir
2- Department of Statistics, Faculty of Mathematical Sciences, University of Mazandaran, Babolsar, Iran ,
Abstract: (240 Views)
Background: This study employs hybrid machine learning algorithms to analyze the number of patients resulting from road accidents in hospitals in Golestan Province over a five-year period from March 2020 to March 2025. It also aims to predict the number of traffic accident patients from March 2025 to March 2027.
Methods: In this retrospective study, a five-year dataset covering March 2020 to March 2025 was used. Hybrid machine-learning algorithms, including SARIMA-LSTM and CNN-LSTM, were applied for analysis and prediction in comparison with the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The performance of these algorithms was evaluated using root mean square error (RMSE), mean absolute error (MAE), and root mean square logarithmic error (RMSLE).
Results: The observed number of patients referred following road accidents in Golestan Province increased from 13,679 in 2020 to 20,323 in 2025. A statistically significant difference was observed in patient numbers between men and women (p-value < 0.001), with five-year increases of 45% for men and 60% for women. For predictions covering March 2025– March 2027, the CNN-LSTM model achieved the lowest error metrics (MAE (%) = 4.5, RMSE = 6.50, RMSLE = 0.21), followed by SARIMA-LSTM (MAE (%) = 8.60, RMSE = 12.45, RMSLE 0.65= 0.15), whereas the conventional SARIMA model exhibited the highest errors (MAE (%) = 12.69, RMSE = 15.37, RMSLE = 0.78).
Conclusion: These findings may assist policymakers and health managers in improving health services through optimal resource allocation and enhanced planning. Furthermore, the use of machine-learning models in hospitals is recommended to support the management and prediction of traffic-accident-related patient volumes.
Methods: In this retrospective study, a five-year dataset covering March 2020 to March 2025 was used. Hybrid machine-learning algorithms, including SARIMA-LSTM and CNN-LSTM, were applied for analysis and prediction in comparison with the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The performance of these algorithms was evaluated using root mean square error (RMSE), mean absolute error (MAE), and root mean square logarithmic error (RMSLE).
Results: The observed number of patients referred following road accidents in Golestan Province increased from 13,679 in 2020 to 20,323 in 2025. A statistically significant difference was observed in patient numbers between men and women (p-value < 0.001), with five-year increases of 45% for men and 60% for women. For predictions covering March 2025– March 2027, the CNN-LSTM model achieved the lowest error metrics (MAE (%) = 4.5, RMSE = 6.50, RMSLE = 0.21), followed by SARIMA-LSTM (MAE (%) = 8.60, RMSE = 12.45, RMSLE 0.65= 0.15), whereas the conventional SARIMA model exhibited the highest errors (MAE (%) = 12.69, RMSE = 15.37, RMSLE = 0.78).
Conclusion: These findings may assist policymakers and health managers in improving health services through optimal resource allocation and enhanced planning. Furthermore, the use of machine-learning models in hospitals is recommended to support the management and prediction of traffic-accident-related patient volumes.
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