This study aims to investigate the effectiveness of local interpretable model-agnostic explanation (LIME) and Shapley additive explanation (SHAP) approaches for chronic heart disease detection. The efficiency of LIME and SHAP are evaluated by analyzing the diagnostic results of the XGBoost model and the stability and quality of counterfactual explanations. Firstly, 1025 heart disease samples are collected from the University of California Irvine. Then, the performance of LIME and SHAP is compared by using the XGBoost model with various measures, such as consistency and proximity. Finally, Python 3.7 programming language with Jupyter Notebook integrated development environment is used for simulation. The simulation result shows that the XGBoost model achieves 99.79% accuracy, indicating that the counterfactual explanation of the XGBoost model describes the smallest changes in the feature values for changing the diagnosis outcome to the predefined output.