Comparison of Machine Learning Models for Prediction of Initial Intravenous Immunoglobulin Resistance in Children With Kawasaki Disease

Comparison of Machine Learning Models for Prediction of Initial Intravenous Immunoglobulin Resistance in Children With Kawasaki Disease

We constructed an optimal machine learning (ML) method for predicting intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) using commonly available clinical and laboratory variables. We retrospectively collected 98 clinical records of hospitalized children with KD (2–1...

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Bibliographic Details
Main Authors: Yasutaka Kuniyoshi, Haruka Tokutake, Natsuki Takahashi, Azusa Kamura, Sumie Yasuda, Makoto Tashiro
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-12-01
Series:Frontiers in Pediatrics
Subjects:
area under the curve
extreme gradient boosting
support vector machine
logistic regression
nested cross-validation
predictive model
Online Access:https://www.frontiersin.org/articles/10.3389/fped.2020.570834/full

Internet

https://www.frontiersin.org/articles/10.3389/fped.2020.570834/full

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