Development and validation of an explainable machine learning-based prediction model for primary Kawasaki disease complicated with coronary artery aneurysms.

Background: Kawasaki disease (KD) can lead to coronary artery aneurysms (CAA) in approximately 1 in 5 untreated children despite intravenous immunoglobulin (IVIG) therapy in the acute phase. The aim of this study is to develop and validate an explainable machine learning (ML)-based prediction model for CAA in KD.

Methods: This study retrospectively analyzed the clinical data of children diagnosed with primary KD at Nanchong Central Hospital, Sichuan Province between 2015 and 2023. Six models, including support vector machine (SVM), K-nearest neighbors (KNN), least absolute shrinkage and selection operator (Lasso), extreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP), based on ML algorithms were developed. The model with optimal performance was validated and the explainable SHapley Additive exPlanations (SHAP) analysis was used.

Results: A total of 327 children diagnosed with KD were included in the training set and validation set. Receiver operator characteristic curve analysis showed that XGBoost based model exhibited an optimal performance among the six models. Moreover, for a given CAA positive sample, the sum of the SHAP values of all variables of XGBoost represented the individual deviation from the mean predicted from the entire dataset.

Conclusions: The XGBoost algorithm-based explainable model might be used to predict the occurrence of CAA in children with KD.