Using Explainable Machine Learning and Integrons to Predict Antimicrobial Resistance in Common Infection-Causing Bacteria
Abstract
Antimicrobial resistance (AMR) poses a significant global threat to public health. In recent years, machine learning (ML) algorithms have emerged as tools for predicting AMR patterns and guiding antibiotic treatment decisions. This study explores the predictive capabilities of three ML algorithms, namely Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest (RF), with the incorporation of integron features along with AMR genes, to construct a web-based in silico antibiogram tool. Utilizing a comprehensive dataset comprising bacterial isolates - Acinetobacter baumannii, Escherichia coli, and Klebsiella pneumoniae and their corresponding resistance profiles to five antibiotics of interest - cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, and levofloxacin, the study train and evaluate the performance of the ML models. Generally, XGBoost and RF perform better than SVM with AUC score up to 0.93. Central to this approach is the integration of integron features, which play a pivotal role in mediating horizontal gene transfer and facilitating the dissemination of resistance genes among bacterial populations. Building upon the robust models, this study developed a user-friendly web application that enables healthcare practitioners and researchers to input whole-genome sequences and rapidly obtain predictions regarding antibiotic resistance profiles based on integrated ML models.
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