Hospital Outpatient Volume Prediction Model Based on Gated Recurrent Unit Optimized by the Modified Cheetah Optimizer

Precise outpatient volume prediction holds significant importance in hospital management. While the Gated Recurrent Unit (GRU) is a frequently utilized deep learning technique for forecasting hospital outpatient volumes, creating a proficient GRU model necessitates the fine-tuning of pertinent GRU parameters. The adjustment of suchparameters relies heavily on an individual’s practical experience and prior knowledge. The recently proposed Cheetah optimizer is a novel intelligent algorithm with unique optimization capabilities. The Cheetah optimizer holds significant research potential; however, additional investigations are warranted, as it may be vulnerable to issues related to local optimization. In the present study, the selection of hyperparameters for the GRU model was optimized through the utilization of the Modified Cheetah Optimization (MCO) algorithm, and a combined MCO-GRU model was established. Using the Successive Variational Mode Decomposition (SVMD) method to decompose outpatient volume sample data, the parameters of the GRU model were optimized with the MCO method to construct a hybrid forecasting model. This yielded the smallest Root Mean Square Error (RMSE) for the proposed model, with a value of 0.0843. Additionally, the results indicate that in comparison to SVMD, Long Short-Term Memory (LSTM), GRU, Particle Swarm Optimization-GRU (PSO-GRU), and Cheetah Optimization-GRU (CO-GRU), the proposed model significantly enhanced the accuracy of outpatient volume forecasting.