Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic

Arima model, Sarima model, and dynamic Bayesian network model

Bibliographic Data

ID22073182
AuthorsPengyu Yang (0000-0001-7554-5281, Sichuan University), Pengfei Cheng (0000-0002-3286-399X, Second Affiliated Hospital of Zhejiang University), Na Zhang (0000-0001-8065-3412), N J Zhang (0009-0004-6241-2251, Hainan Medical University), Ding Luo (0000-0003-2661-0926), Luo Ding (Hainan Medical University), Baichao Xu, Xu Baichao (Hainan Medical University), Hua Zhang (0009-0007-7319-3252, Hainan Medical University, corresponding author)
Year2024
Volume12
Pages1401161-1401161
Publication date2024-06-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1401161
PMID39022407
OpenAlexW4400099339
LanguageEN
Citations received3
References cited40

Introduction Rescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models. Methods In this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination ( R 2 ) as performance metrics. Results In this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R 2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R 2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R 2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R 2 , during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA’s predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions. Discussion While the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies

Autoregressive integrated moving average · Bayesian probability · Dynamic Bayesian network · Geography · Machine learning · Mean absolute percentage error · Mean squared error · Moving average · Statistics · Time series · Bayesian Modeling and Causal Inference · Computer Science · Maritime Navigation and Safety · Mathematics · Trauma and Emergency Care Studies · Artificial Intelligence

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