Machine learning model for predicting urinary tract infection risk in febrile children under 3 years of age.

Objective: Urinary tract infection (UTI) is a common childhood infectious disease. Accurate prediction of UTI risk in febrile children enables timely intervention and helps avoid long-term complications such as renal scarring. Methods: 1,556 cases of febrile children under 3 years of age were retrospectively analyzed, and feature variables were screened using LASSO regression. Seven machine learning (ML) algorithms, including Random Forest, were used to construct the UTI prediction model. The model performance was evaluated based on comprehensive indices, including area under the curve (AUC), calibration curve, and decision curve analysis, from which the optimal prediction model was selected. The SHAP method was applied to analyze the decision-making mechanism of the model. Results: Among the seven ML models, Random Forest performed best, achieving an AUC of 0.88 in the test set, an AUPRC of 0.824, optimal calibration (ICI = 0.12), and decision curve analysis showed superior performance compared to other ML algorithms. Through LASSO regression screening and SHAP analysis, seven core predictors were established: age, WBC count, previous UTI episodes, PLT, fever peak, CRP, prenatally detected renal abnormalities. These key indicators helped to construct an accurate prediction system for UTI risk in febrile children. Conclusions: The ML model constructed in this study can accurately predict UTI risk in febrile children under 3 years of age. The visual decision interpretation achieved through the SHAP framework can assist clinicians in quickly identifying high-risk children.