Abstract:Objective：To develop a model utilizing the support vector machine（SVR）machine learning algorithm and assess its efficacy in predicting outpatient visits and discharges. Methods：The study used the number of outpatient visits and discharges from a three-class hospital from January 2016 to April 2024 as the research object，and different kernel functions were used to build three different models（linear kernel SVR，multi-thread SVR，Gaussian kernel SVR）. Results：The RMSE，MAE，and MAPE values for predicting outpatient visits using the linear kernel SVR were 13470，10510 and 9.8%. The RMSE，MAE，and MAPE values of the number of discharged patients were 1005，753，and 10.8%，respectively.，which were all better than the prediction results of the multi-thread SVR and Gaussian kernel SVR models.Conclusion：In the calculation of the three SVR models，the linear kernel SVR model had the best prediction performance and is more suitable for predicting the number of outpatient and inpatient visits in a hospital，which can provide more solid data support for hospital management decisions on medical resource allocation and scheduling，and effectively ensure the efficient and orderly operation of the hospital.