Machine learning based prediction of antimicrobial resistance

Introduction: Methods: A retrospective analysis conducted using routine microbiology laboratory data collected between 2019 and 2024. Antimicrobial susceptibility testing included multiple agents across major antimicrobial classes, allowing classification of isolates into defined resistance phenotypes. Multidrug-resistant (MDR), extensively drug-resistant (XDR), and pan-drug-resistant (PDR) profiles were determined using standard class-based definitions. In parallel, several supervised machine learning models were developed and evaluated, including Random Forest, Support Vector Machine (SVM), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Extra Trees Classifier, Classifier Chains (multilabel classification), Deep Neural Network (DNN), Voting Ensemble, and Convolutional Neural Network (CNN). Models were trained using the training dataset and subsequently evaluated on the testing dataset to assess predictive performance and generalizability. Results: Over the five-year study period, Conclusions: Collectively, these findings underscore the increasing clinical burden of