Building a diagnostic prediction model for severe Mycoplasma pneumoniae pneumonia in children using machine learning
Chuxiong Gong, Helang Yue, Quan Li, Yanfei Yang, Hongyan Li, Tingting Hao, Hongrui Wu, Yanwei Xu, Qiyin Huang, Xingzhu Liu, Yuqin Wu
Abstract
Objective: pneumonia (MPP) has shown a global pandemic trend. The increasing incidence of severe MPP (SMPP) leads to complications and even deaths, severely impacting prognosis and quality of life. Our study aimed to use machine learning to construct an early diagnostic model for severe MPP in children. It supports early prediction, prevention, and individualized precise treatment of SMPP. Methods: We collected medical records from 372 MPP cases. We compared case characteristics between groups with and without SMPP and used a random forest to screen key factors. We then constructed a multivariate logistic prediction model. We evaluated the model with ROC curves, calibration curves, and DCA. Five-fold cross-validation tested prediction stability. Results: We identified ESR, PCT, IL-6, and lung auscultation as key factors to construct the prediction model. The model's ROC was 0.964 (95% CI: 0.945-0.983). Calibration curves and DCA confirmed model accuracy. Five-fold cross-validation validated internal stability. Conclusion: Our study developed a prediction model with good efficacy for early SMPP risk assessment. Our research provides a basis for clinical early prediction and prevention of SMPP, reducing its risk and offering a foundation for individualized treatment and improved long-term outcomes in affected children.