A Machine Learning Model Integrating Clinical and Molecular Data for Predicting Macrolide‐Resistant Mycoplasma pneumoniae Pneumonia in Children

Background The escalating prevalence of macrolide‐resistant Mycoplasma pneumoniae (MRMP) among pediatric populations presents increasing challenges for empirical antibiotic selection. Prompt identification of resistant cases remains essential for optimizing therapeutic strategies. This investigation aimed to develop and validate a predictive framework combining 23 S rRNA mutation status with routine clinical parameters to assess resistance probability in children with M. pneumoniae pneumonia. Methods A cohort of 188 hospitalized children diagnosed with M. pneumoniae infection was enrolled. Based on detection of the A2063G point mutation within the 23 S rRNA gene, participants were categorized into resistant ( n = 118) and sensitive ( n = 70) groups. Relying exclusively on baseline clinical and laboratory parameters obtained upon admission (treatment‐related outcomes were strictly excluded), we employed machine learning algorithms to construct an early prediction model. Model performance and generalizability were strictly evaluated using a stratified 5‐fold cross‐validation strategy. Results Comparative analysis of baseline characteristics revealed distinct profiles between cohorts. Feature importance analysis derived from the Random Forest model identified patient age, elevated monocyte counts, and decreased prealbumin concentrations as the top early predictors for resistance. The optimized Gradient Boosting model demonstrated robust discrimination with an area under the curve (AUC) of 0.817 in 5‐fold cross‐validation, significantly outperforming standard logistic regression. Conclusion Integration of clinical parameters with molecular markers through machine learning methodology enables effective risk stratification for macrolide resistance in pediatric M. pneumoniae pneumonia. This approach facilitates earlier recognition of potential resistant infections, thereby informing targeted antimicrobial selection and supporting judicious antibiotic utilization in clinical practice.