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

Frontiers in Pediatrics

article

Yasutaka Kuniyoshi¹  Natsuki Takahashi¹  Azusa Kamura¹  Sumie Yasuda¹  Makoto Tashiro¹ 

[1] Department of Pediatrics, Tsugaruhoken Medical COOP Kensei Hospital

关键词: area under the curve;    extreme gradient boosting;    support vector machine;    logistic regression;    nested cross-validation;    predictive model;   

DOI  :  10.3389/fped.2020.570834

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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 (2–109 months of age). We found that 20 (20%) children were resistant to initial IVIG therapy. We trained three ML techniques, including logistic regression, linear support vector machine, and eXtreme gradient boosting with 10 variables against IVIG resistance. Moreover, we estimated the predictive performance based on nested 5-fold cross-validation (CV). We also selected variables using the recursive feature elimination method and performed the nested 5-fold CV with selected variables in a similar manner. We compared ML models with the existing system regardless of their predictive performance. Results of the area under the receiver operator characteristic curve were in the range of 0.58–0.60 in the all-variable model and 0.60–0.75 in the select model. The specificities were more than 0.90 and higher than those in existing scoring systems, but the sensitivities were lower. Three ML models based on demographics and routine laboratory variables did not provide reliable performance. This is possibly the first study that has attempted to establish a better predictive model. Additional biomarkers are probably needed to generate an effective prediction model.

【 授权许可】

CC BY