An integrated and interpretable machine learning framework for Kawasaki disease diagnosis and risk prediction.

ABSTRACT:

Background

Early identification of Kawasaki disease (KD) and accurate prediction of its associated complications are critical for optimizing treatment strategies and improving clinical outcomes. While machine learning has shown promise in KD-related studies, most existing models are limited to single tasks with varying feature sets, lacking an integrated framework. This fragmentation hinders clinical applicability and constrains generalizability. Therefore, this study aimed to develop a unified and interpretable machine learning framework for KD diagnosis and risk prediction, with the goal of enhancing clinical relevance and real-world applicability.

Methods

We retrospectively collected data from 2,133 febrile pediatric patients treated at The First Affiliated Hospital of Un

SUBMITTER: Wang D 

PROVIDER: S-EPMC12552161 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature