Objective To perform data-driven, assisted prediction of health insurance reimbursement ratios for the major thoracic surgery group in CHS-DRG, in addition to providing an optional solution for health insurance providers and medical institutions to accurately and effectively predict the references of health insurance payments for the patient group. Methods Using the information on major thoracic surgery cases from a large tertiary hospital in Sichuan province in 2020 as a sample, 70% of the total dataset was used as a training dataset and 30% as a test dataset. This data was used to predict health insurance spending through a multiple linear regression model and an improved machine learning method that is based on feature selection. Results When the number of filtered features was the same via three machine learning methods including random forest, logistic regression, and support vector machine, there was no significant difference in the prediction effectiveness. The model with the best prediction effect had an accuracy of 78.96%, sensitivity of 83.93%, specificity of 71.27%, precision of 0.818 8, AUC value of 0.841 4, and a Kappa value of 0.610 8. Conclusion The basic characteristics such as the number of disease diagnoses and surgical operations, as well as the age of patients affect the reimbursement ratio. The cost of materials, drugs, and treatments has a greater impact on the reimbursement ratio. The combined method of feature selection and machine learning outperforms traditional statistical linear models. When dealing with a larger dataset that has many features, selecting the right number can enhance the prediction ability and efficiency of the model.
