Objective To construct the prediction model of hospitalization expenses for ischemic heart disease, reveal the key factors affecting hospitalization expenses, and analyze the interaction between variables. Methods Patients from Sichuan medical insurance comprehensive service platform from January 2020 to December 2021 were extracted. The training set and test set were divided according to the ratio of 7∶3. Six machine learning models were constructed and trained by ten-fold cross validation, and was explained by SHAP theory. Results XGBoost model had the best performance among these models, with a R2 of 0.60, RMSE of 9 969.71 yuan, and MAE of 5 242.90 yuan in the test set. SHAP results showed that the five variables with the greatest impact on hospitalization expenses were surgery, length of stay, hospital grade, disease type and DRG. Hospitalization costs were higher when grade 3 or 4 procedures were performed, the length of stay was prolonged, the hospitalization was in a tertiary hospital, and payments were made for acute myocardial infarction and non-DRG. With the prolongation of hospital stay, the hospitalization expenses increased faster when the patient had grade 4 surgery and was in a tertiary hospital. In addition, DRG payment will reduce the length of hospital stay and the hospitalization expenses of patients with different disease types. Conclusion The interpretable XGBoost model constructed in this study has a good predictive performance for the hospitalization expenses of patients with ischemic heart disease. Combined with SHAP theory, it can effectively identify the key factors affecting the hospitalization expenses and analyze their interactions.