A machine learning model for predicting severe mycoplasma pneumoniae pneumonia in school-aged children

作者：Yingying Ye, Zhenpeng Gao, Zhiling Zhang, Jianlong Chen, Chu Chu, Weifang Zhou · 发表于：BMC Infectious Diseases · 年份：2025 · DOI：10.1186/s12879-025-10958-8 · 被引用次数：16 · 研究领域：Pneumonia and Respiratory Infections、vaccines and immunoinformatics approaches、Immune responses and vaccinations

OBJECTIVE: To develop an interpretable machine learning (ML) model for predicting severe Mycoplasma pneumoniae pneumonia (SMPP) in order to provide reliable factors for predicting the clinical type of the disease. METHODS: We collected clinical data from 483 school-aged children with M. pneumoniae pneumonia (MPP) who were hospitalized at the Children's Hospital of Soochow University between September 2021 and June 2024. Difference analysis and univariate logistic regression were employed to identify predictors for training features in ML. Eight ML algorithms were used to build models based on the selected features, and their effectiveness was validated. The area under the curve (AUC), accuracy, five-fold cross-validation, and decision curve analysis (DCA) were utilized to evaluate model performance. Finally, the best-performing ML model was selected, and the Shapley Additive Explanations (SHAP) method was applied to rank the importance of clinical features and interpret the final model. RESULTS: After feature selection, 30 variables remained. We constructed eight ML models and assessed their effectiveness, finding that the CatBoost model exhibited the best predictive performance, with an AUC of 0.934 and an accuracy of 0.9175. DCA was used to compare the clinical benefits of the models, revealing that the CatBoost model provided greater net benefits than the other ML models within the threshold probability range of 34% to 75%. Additionally, we applied the SHAP method to inter...