A machine learning-based web application aids Kawasaki disease diagnosis and predicts its outcomes
Abstract
Unless Kawasaki disease (KD) is promptly treated with intravenous immunoglobulin (IVIG), patients may develop IVIG-unresponsiveness and aneurysm. We analysed 628 KD and 155 KD-like cases to develop machine learning models for having KD (i.e., being diagnosed with KD), IVIG-unresponsiveness and aneurysm formation. We applied LASSO logistic regression to illness-day-indexed data incorporating family/previous histories and laboratory variables (e.g., neutrophils, total bilirubin, C-reactive protein, and AST). Optional inputs included BNP, IgG, fibrinogen, and echocardiographic Z scores. On Day 4, all models achieved AUCs > 0.7 in the test dataset: having KD (0.779), IVIG-unresponsiveness (0.772), and aneurysm formation (0.710). On Day 5, the model achieved a positive predictive value > 90% for having KD. When combined with predictions of unresponsiveness or aneurysm, 90% of complicated KD cases were identified. These models were implemented in a web-based and intranet-integrated application, enabling early diagnosis and risk stratification of KD across resource-limited and resource-rich settings.
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BibTeX
@article{nagao2026machine,
title = {{A machine learning-based web application aids Kawasaki disease diagnosis and predicts its outcomes}},
author = {Yoshiro Nagao and Nobuhisa Kita and Shunsuke Fujii and Sumito Sameshima and Yoshimi Nishimura and Hiroe Okubo and Hisashi Endo and Ichiro Yamane},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-77440-6},
url = {https://www.nature.com/articles/s41598-026-77440-6},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A machine learning-based web application aids Kawasaki disease diagnosis and predicts its outcomes
AU - Yoshiro Nagao
AU - Nobuhisa Kita
AU - Shunsuke Fujii
AU - Sumito Sameshima
AU - Yoshimi Nishimura
AU - Hiroe Okubo
AU - Hisashi Endo
AU - Ichiro Yamane
PY - 2026
DA - 2026/10/24
JO - Scientific Reports
DO - 10.1038/s41598-026-77440-6
UR - https://www.nature.com/articles/s41598-026-77440-6
AB - Unless Kawasaki disease (KD) is promptly treated with intravenous immunoglobulin (IVIG), patients may develop IVIG-unresponsiveness and aneurysm. We analysed 628 KD and 155 KD-like cases to develop machine learning models for having KD (i.e., being diagnosed with KD), IVIG-unresponsiveness and aneurysm formation. We applied LASSO logistic regression to illness-day-indexed data incorporating family/previous histories and laboratory variables (e.g., neutrophils, total bilirubin, C-reactive protein, and AST). Optional inputs included BNP, IgG, fibrinogen, and echocardiographic Z scores. On Day 4, all models achieved AUCs > 0.7 in the test dataset: having KD (0.779), IVIG-unresponsiveness (0.772), and aneurysm formation (0.710). On Day 5, the model achieved a positive predictive value > 90% for having KD. When combined with predictions of unresponsiveness or aneurysm, 90% of complicated KD cases were identified. These models were implemented in a web-based and intranet-integrated application, enabling early diagnosis and risk stratification of KD across resource-limited and resource-rich settings.
SN - 2045-2322
ER -