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

ABSTRACT:

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 w

SUBMITTER: Song Z 

PROVIDER: S-EPMC11921264 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature