Prediction of antimicrobial resistance of Klebsiella pneumoniae through machine learning and revelation of significant role of insertion sequence elements
Abstract
Abstract Background Klebsiella pneumoniae ( K. pneumoniae ) poses critical therapeutic challenges due to multidrug resistance (MDR). While antibiotic resistance genes (ARGs) are primary determinants, their expression and phenotypic impact are modulated by mobile genetic elements such as insertion sequence (IS) elements. However, the contribution of IS elements to antimicrobial resistance (AMR) prediction remains largely unexplored in K. pneumoniae . Methods We retrieved genome sequences and corresponding antimicrobial susceptibility phenotypes for 2,732 K. pneumoniae isolates from the PATRIC database (2004–2024). We evaluated ARGs, IS elements, IS-ARG pairs, and curated key feature sets using six machine learning (ML) algorithms to predict resistance phenotypes for 15 antibiotics. Model performance was assessed using accuracy, recall, positive predictive value (PPV), F1-score, and the area under the receiver operating characteristic (ROC) curve (AUC). SHAP analysis was employed to interpret feature contributions, and enrichment of ARGs and IS elements in key feature sets was examined. Hierarchical clustering based on IS-ARG co-occurrence patterns was performed to explore associations with MDR profiles. Results The Random Forest (RF) and Support Vector Machine (SVM) outperformed other models, achieving mean accuracies of 92.22% and 88.83% across all antibiotics. SVM utilizing the key feature set attained an AUC of 99.87% for trimethoprim. IS elements enabled RF to reach the…
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