Interpretable Machine Learning Model For Heart Disease Prediction

Abstract

In the medical industry, accurately predicting a patient's likelihood of heart disease requires a high-performance model and explaining how the model arrived at its conclusion. To address this, a study has proposed a way to interpret machine learning models using SHAP and LIME. Four models have been created: Vector Machine, Random Forest, XGBoost, and k-Nearest Neighbor. The SVM and XGBoost models exhibit the highest f1-score performance, reaching up to 88%. These models can then be utilized during the interpretation stage with the aid of SHAP and LIME. Based on the SHAP visualization results, it is evident that the predictions made include various significant variables. Meanwhile, LIME explains the classification of each data point. Additionally, it confirms that SHAP and LIME are valuable tools for interpreting models.