Prediction of Antibiotic Susceptibility in E. coli Isolates Using Machine Learning.

Abstract

Antimicrobial resistance (AMR) poses a significant global health threat, resulting in 4.96 million deaths in 2019, with projections reaching 10 million by 2050. This resistance, primarily due to the overuse of antibiotics, complicates the treatment of infections caused by various microorganisms, including the gram-negative bacterium Escherichia coli. Traditional culture-based methods for detecting AMR are slow and imprecise, hindering timely clinical decision-making. In contrast, whole genome sequencing offers a faster, more accurate alternative for AMR detection. A novel machine learning study leveraging whole genomic sequencing data to predict the phenotypic susceptibility of Escherichia coli to ciprofloxacin is presented. Using a novel dataset of 256 bacterial genomes and related susceptibility data, features were generated based on AMRFinderPlus findings and k-mer frequencies. The machine learning models, Random Forest and XGBoost, were evaluated using a five-fold cross-validation approach. Results showed that combining AMRFinderPlus and k-mer frequency features could achieve more than 90% accuracy using the XGBoost gradient boosting model. These findings suggest that the best results may be achieved using reference-free features combined with known gene markers.