Abstract:  
Abstract： Hospitalization costs are one of the factors that affect treatment choices and prognosis for breast cancer patients. Accurate prediction of hospitalization costs and personalized analysis of cost-influencing factors are crucial for efficient resource allocation and optimization of medical services. Addressing the issues of weak generalization ability and poor interpretability in single-model hospitalization cost prediction tasks， this paper proposes an interpretable stacking method. This method fully integrates the feature extraction capabilities of multiple models to achieve accurate prediction of hospitalization costs for breast cancer patients. The method employs a two-layer model fusion structure， where the first layer selects four base models and utilizes Bayesian optimization and five-fold cross validation techniques to optimize parameters， enhancing the predictive performance of each model. The final hospitalization cost prediction is then generated by the second-layer model. Additionally， this paper uses SHAP and LIME methods to analyze the results of breast cancer hospitalization cost predictions from the global and individual perspectives. The experimental results on a five-year dataset of breast cancer in patients from a certain hospital demonstrate that stacking method achieves an R2 metric of 0.877 in the cost prediction task， outperforming other related studies. The interpretable analysis indicates that length of stay and treatment method are the primary factors influencing overall costs， but there are variations in the influencing factors among different patients. This provides valuable insights for a deeper understanding of key factors affecting hospitalization costs.

Key words: Key words： hospitalization cost prediction, interpretable stacking method, SHAP, LIME, machine learning