An interpretable machine learning model for predicting febrile seizures following enterovirus infection in children.

OBJECTIVE: This study aims to develop an interpretable machine learning model for predicting the risk of FS in children with enterovirus (EV) infections and to implement it for clinical application. METHODS: This retrospective study included 446 hospitalized children with EV infection (144 FS, 302 non‑FS). LASSO regression and BORUTA algorithm selected 15 key predictors from 53 clinical variables. Six models (logistic regression, KNN, Naive Bayes, MLP, random forest, XGBoost) were constructed and evaluated using AUC, sensitivity, specificity, F1 score, and decision curve analysis. SHAP values provided interpretability, and a Shiny web‑based calculator was developed. RESULTS: The XGBoost model demonstrated the best predictive performance: the training set AUC reached 0.972 (95% CI: 0.958-0.987), with sensitivity of 0.892 and specificity of 0.905. The internal validation set achieved an AUC of 0.842 (95% CI: 0.757-0.926). DCA confirmed its strong clinical applicability. SHAP analysis identified key features contributing to the model: fever duration, disease course, immunoglobulin M, neutrophil count, fibrinogen, CD8+ T-cell percentage, aspartate aminotransferase, CD3+ T-cell percentage, procalcitonin, presence of hand-foot herpes lesions, CD19+ B-cell percentage, erythrocyte sedimentation rate, lymphocyte count, serum ferritin level, and alanine aminotransferase. The 'Shiny' calculator facilitates personalized risk assessment. CONCLUSION: The XGBoost predictive model developed in this study demonstrated both high accuracy and clinical interpretability. The associated web-based calculator offers a new tool for risk stratification and management of FS in children with EV infections.