An electrocardiogram-based machine learning model for distinguishing complete Kawasaki disease.
Journal:
medRxiv
Published Date:
May 6, 2026
Abstract
Kawasaki disease (KD) is a systemic vasculitis in young children, and early diagnosis remains challenging when clinical features are incomplete or overlap with those of other febrile illnesses. Because electrocardiography (ECG) is noninvasive and widely available, we investigated whether ECG-derived features could help distinguish complete KD from pediatric patients with fevers. We conducted a single-center retrospective study of hospitalized febrile children aged 1-8 years who underwent digital 12-lead ECG recording during the initial evaluation at a hospital. Five amplitude features and six timing features extracted from the ECG were used to develop a logistic regression model to distinguish between complete KD and other febrile illnesses. The model succeeded in the discrimination between KD and non-KD groups. The prediction performance was not strongly correlated with the age and body temperature. Wave amplitudes and RR interval were suggested as the important features for the discrimination. These findings suggest that ECG-derived features may provide adjunctive information for distinguishing complete KD from other febrile illnesses.
