ORIGINAL RESEARCH article
Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders: Autoinflammatory Disorders
A machine learning algorithm to predict treatment effectiveness for Kawasaki disease in China: A retrospective model development and validation study
Provisionally accepted
Li Xue Mei1
Zihan Zhou1Jingyi Fan1Lin Zhao1Ruidi Xu1Dong Li1Xu Ma1
Lu Sun2
Yujian Wu2Zhouping Wang2*
Ce Wang1*- 1China Medical University, Shenyang, China
- 2Guangzhou Women and Children's Medical Center, Guangzhou, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background Kawasaki disease (KD) is the primary cause of acquired heart disease in children. Intravenous immunoglobulin (IVIG) is the first-line therapy for KD; however, IVIG resistance can occur. Reliable treatment efficacy prediction tools for Chinese patients are lacking, which this study aimed to address. Methods This retrospective cohort study enrolled patients diagnosed with KD admitted to Shengjing Hospital of China Medical University and collected data on 36 demographic, clinical, and laboratory parameters. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify key predictive variables. The dataset was divided into training (70%) and validation (30%) sets. Ten models were trained through 10-fold cross-validation, and the training set data were balanced using the ROSE method for oversampling. The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Patients with KD admitted to Guangzhou Women and Children's Medical Centre, Guangzhou Medical University, between January 2023 and December 2024 were enrolled as an external validation cohort. Results The CatBoost machine learning algorithm achieved the best comprehensive results (AUC: 0·960; sensitivity: 0·883; specificity: 0·889, and accuracy: 0·887). The internal validation results with CatBoost were AUC: 0·862; 95% confidence interval [CI]: 0·6453–0·7651; sensitivity: 0·716; specificity: 0·877; and accuracy: 0·861. The external validation results were AUC: 0·834; 95% CI: 0·783–0·884; sensitivity: 0·817; specificity: 0·838, and accuracy: 0·835. Conclusions This is a provisional file, not the final typeset article We present a machine learning model that can predict the risk of IVIG non-responsiveness in patients with KD in China. This model may help doctors develop personalised treatment strategies, thus improving the prognosis of KD.
Keywords: kawasaki disease, IVIG resistance, machine learning, SHAP Algorithm, Intravenous Immunoglobulin
Received: 16 May 2025; Accepted: 12 Nov 2025.
Copyright: © 2025 Mei, Zhou, Fan, Zhao, Xu, Li, Ma, Sun, Wu, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhouping Wang, wang_zhouping@gwcmc.org
Ce Wang, crease.sy@163.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.