Development and validation of a machine learning-driven framework for differentiating pediatric bronchopneumonia from
Zihan Cai1, Shoupeng Ding2, Jinghua Gao3
1Department of Medical Laboratory, Siyang Hospital, Siyang, 223700, China.
Objective:
This investigation aims to establish and substantiate a machine learning-driven predictive framework designed to precisely distinguish between pediatric bronchopneumonia and lobar pneumonia. This endeavor seeks to elevate the accuracy of early clinical support, refine treatment decision-making, and curtail superfluous medical interventions.
Methods:
This study was executed at Siyang Hospital, enrolling 2304 pediatric patients diagnosed with either bronchopneumonia or lobar pneumonia from January 2020 to December 2024. Participants were randomized in a 7:3 ratio into training (n = 1612) and testing (n = 692) sets, supplemented by an external validation set (n = 454) to evaluate the model's generalizability. Hematological and serum biochemical parameters were gathered, with feature selection conducted using eXtreme Gradient Boosting (XGBoost), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest algorithms. A suite of twelve machine learning models-including Random Forest, Gradient Boosting, and Support Vector Machines-was developed, with parameters fine-tuned through five-fold cross-validation. Model efficacy was assessed via receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, and F1 score, while feature significance was quantified using SHAP values. A nomogram was formulated based on critical features, its clinical value affirmed through calibration curves, and decision curve analysis (DCA). Statistical evaluations incorporated Mann-Whitney U tests, chi-square tests, and DeLong tests, with a threshold of P < 0.05 denoting significance.
Results:
Notable disparities emerged between the bronchopneumonia (n = 1868) and lobar pneumonia (n = 436) cohorts across several hematological markers, such as large platelet count (P-LCT), Lymphocyte percentage (LYM%), and creatinine (CREA) (P < 0.01). Feature selection pinpointed P-LCT, LYM%, and CREA as key predictors. The Gradient Boosting model demonstrated exemplary performance, yielding an AUC of 0.947 (95% CI 0.934-0.960) in the training set, 0.968 (95% CI 0.954-0.982) in the testing set, and 0.989 (95% CI 0.981-0.997) in the external validation set, underscoring its outstanding discriminative prowess and robust generalizability. SHapley Additive exPlanations (SHAP) analysis underscored P-LCT (Mean Absolute SHAP: 0.057) and LYM% (0.065) as predominant predictors, exhibiting a strong correlation with disease severity. The nomogram attained an AUC of 0.962, with impeccable calibration (C-index = 0.962), and DCA substantiated considerable net benefit at moderate risk thresholds.
Conclusion:
The Gradient Boosting model, as delineated in this study, markedly advances the differential diagnosis of pediatric bronchopneumonia and lobar pneumonia, delivering high precision and resilience. It serves as an efficacious and dependable clinical decision-support instrument. By incorporating pivotal biomarkers like P-LCT and LYM%, this model illuminates pathophysiological traits, enhances antibiotic stewardship, and guides hospitalization choices, thereby diminishing healthcare resource wastage and ameliorating patient outcomes. These insights furnish vital backing for precision medicine and acute care management.
