A machine learning algorithm to predict treatment effectiveness for Kawasaki disease in China: a retrospective model development and validation study.

ABSTRACT:

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

SUBMITTER: Li X 

PROVIDER: S-EPMC12689519 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature