Clinical and Experimental Pediatrics

Original Article

Infection

Construction and validation of predictive models for intravenous immunoglobulin–resistant Kawasaki disease using an interpretable machine learning approach

Linfan Deng, Jian Zhao, Ting Wang, Bin Liu, Jun Jiang, Peng Jia, Dong Liu, Gang Li

Clin Exp Pediatr. 2024;67(8):405-414. Published online July 23, 2024

Web of Science 1

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.

DOI: https://doi.org/10.3345/cep.2024.00549

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