In this study, the prediction model based on the Stacking principle is called the Stacking fusion model. Little evidence demonstrates that the Stacking fusion model possesses better prediction performance in the field of heart disease diagnosis than other classification models. Since this model belongs to the family of ensemble learning models, which has a bad interpretability, it should be used with caution in medical diagnoses. The purpose of this study is to verify whether the Stacking fusion model has better prediction performance than stand-alone machine learning models and other ensemble classifiers in the field of heart disease diagnosis, and to find ways to explain this model. This study uses experiment and quantitative analysis to evaluate the prediction performance of eight models in terms of prediction ability, algorithmic stability, false negative rate and run-time. It is proved that the Stacking fusion model with Naive Bayes classifier, XGBoost and Random forest as the first-level learners is superior to other classifiers in prediction ability. The false negative rate of this model is also outstanding. Furthermore, the Stacking fusion model is explained from the working principle of the model and the SHAP framework. The SHAP framework explains this model’s judgement of the important factors that influence heart disease and the relationship between the value of these factors and the probability of disease. Overall, two research problems in this study help reveal the prediction performance and reliability of the cardiac disease prediction model based on the Stacking principle. This study provides practical and theoretical support for hospitals to use the Stacking principle in the diagnosis of heart disease.