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Question: Is there a reliable model to predict intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD)?
Finding: We constructed 5 machine learning models to predict IVIG-resistant KD. Extreme gradient boosting (XGBoost) model was superior to logistic, support vector machine, light gradient boosting machine and multiple layers perception models. The SHAP (SHapley Additive exPlanations) value interpreted the contribution of each feature in XGBoost model.
Meaning: XGBoost model showed the excellent performance to predict IVIG-resistant KD with explainable and visualizable machine learning algorithm. |
