Exploration of the correlation between clinical indicators and prognosis in hospitalized children with pneumonia and construction of a risk prediction model based on machine learning algorithms.

Background: Childhood pneumonia is a leading cause of hospitalization and death in children under 5 years globally. Its prognosis varies individually and is affected by multiple clinical indicators, while traditional assessment lacks quantitative risk stratification tools. Machine learning (ML) enables comprehensive analysis of high-dimensional clinical data, making it valuable for identifying key prognostic factors and building robust prediction models to optimize clinical decision-making. Methods: A total of 582 hospitalized children (1 month-5 years) with community-acquired pneumonia were retrospectively enrolled (January 2022-June 2025). Demographic, laboratory (WBC, CRP, PCT, LYM%, serum albumin), vital sign, and underlying disease data were collected. Adverse prognosis was defined as a composite of prolonged hospitalization (>7 days), PICU admission, or in-hospital death. Patients were randomly split into training ( Results: Adverse prognosis occurred in 121 (20.8%) children. The XGBoost model outperformed RF and LR, with validation-set AUC 0.84 (95% CI: 0.78∼0.90), accuracy 81.1%, sensitivity 78.6%, and specificity 82.3%. Model calibration was verified via Hosmer-Lemeshow test ( Conclusion: The XGBoost-based model effectively identifies high-risk children with pneumonia, with PCT, CRP, and respiratory rate as key predictors. It provides a practical tool for clinical risk stratification and personalized management. The model's cutoffs for PCT (>2 ng/mL) and CRP (>40 mg/L) align with existing pediatric pneumonia predictive scores (e.g., PRIEST score) but offer improved discriminative power by integrating multi-dimensional indicators and ML-driven interactions.