Metaheuristic Approaches Integrated with ANN in Forecasting Daily Emergency Department Visits

Engin Pekel,Samuel Yousefi,Erkan Celik,Muhammet Gul

doi:10.1155/2021/9990906

Engin Pekel, Samuel Yousefi + Show 2 more

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https://doi.org/10.1155/2021/9990906

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The overall service quality level of Emergency Departments (EDs) can be improved by accurate forecasting of patient visits. Accordingly, this study aims to evaluate the use of three metaheuristic approaches integrated with Artificial Neural Network (ANN) in forecasting daily ED visits. To do this, five performance measures are used for evaluating the accuracy of the proposed approaches, including Bayesian ANN, Genetic Algorithm-based ANN (GA-ANN), and Particle Swarm Optimization algorithm-based ANN (PSO-ANN). The outputs of this study show that the PSO-ANN model provides the most dominant performance in both the training and testing process. The lowest error is obtained with a mean absolute percentage error (MAPE) of 6.3%, Mean Absolute Error (MAE) of 42.797, Mean Squared Error (MSE) of 2499.340, Root Mean Square Error (RMSE) of 49.933, and R-squared (R2) of 0.824 on the training dataset. The lowest error with an MAPE of 6.0%, MAE of 40.888, MSE of 2839.998, RMSE of 53.292, and R2 of 0.791 is also obtained on the testing process.

Highlights

Emergency Departments (EDs) are the units that perform very crucial duties within the hospital service system and provide uninterrupted service
While logsig is used in the transfer function, purelin is applied in the activation function. e minimum Mean Squared Error (MSE) is obtained with 45 neurons, and all solutions are presented in Table 2. en, the results of Bayesian Artificial Neural Network (ANN) are solved using this combination
Results of each model are analyzed under five different performance measures called mean absolute percentage error (MAPE), Mean Absolute Error (MAE), MSE, Root Mean Square Error (RMSE), and R-squared

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Introduction

Emergency Departments (EDs) are the units that perform very crucial duties within the hospital service system and provide uninterrupted service. These departments are the sole units where the patient traffic and transfer is the most and overcrowding is felt too much [1]. When this is the case, it is vital to improving the provided service quality level by newly adopted methodologies. E remainder of the study is organized as follows: Section 2 presents both an overview on contemporary real-life case studies of ANN and ED patient visit forecast in the light of four dimensions.

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