Multi-label classification to predict antibiotic resistance from raw clinical MALDI-TOF mass spectrometry data.

ABSTRACT: Antimicrobial resistance (AMR) poses a significant global health challenge, necessitating advanced predictive models to support clinical decision-making. In this study, we explore multi-label classification as a novel approach to predict antibiotic resistance across four clinically relevant bacteria: E. coli, S. aureus, K. pneumoniae, and P. aeruginosa. Using multiple datasets from the DRIAMS repository, we evaluated the performance of four algorithms - Multi-Layer Perceptron, Support Vector Classifier, Random Forest, and Extreme Gradient Boosting - under both single-label and multi-label frameworks. Our results demonstrate that the multi-label approach delivers competitive performance compared to traditional single-label models, with no statistically significant differences in most cases.

SUBMITTER: Astudillo CA 

PROVIDER: S-EPMC11682278 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature