Objective: This paper aims to address the clinical challenge of predicting the outcomes of stroke patients and proposes a comprehensive model called RGX to help clinicians adopt more personalized treatment plans. Impact Statement: The comprehensive model is first proposed and applied to clinical datasets with missing data. The introduction of the Shapley additive explanations (SHAP) model to explain the impact of patient indicators on prognosis improves the accuracy of stroke patient mortality prediction. Introduction: At present, the prediction of stroke treatment outcomes faces many challenges, including the lack of models to quantify which clinical variables are closely related to patient survival. Methods: We developed a series of machine learning models to systematically predict the mortality of stroke patients. Additionally, by introducing the SHAP model, we revealed the contribution of risk factors to the prediction results. The performance of the models was evaluated using multiple metrics, including the area under the curve, accuracy, and specificity, to comprehensively measure the effectiveness and stability of the models. Results: The RGX model achieved an accuracy of 92.18% on the complete dataset, an improvement of 11.38% compared to that of the most advanced state-of-the-art model. Most importantly, the RGX model maintained excellent predictive ability even when faced with a dataset containing a large number of missing values, achieving an accuracy of 84.62%. Conclusion: In summary, the RGX ensemble model not only provides clinicians with a highly accurate predictive tool but also promotes the understanding of stroke patient survival prediction, laying a solid foundation for the development of precision medicine.Copyright © 2024 Jing Fang et al.