Multi-Label Classification for Predicting Antimicrobial Resistance on <i>E. coli</i>

Abstract

Antimicrobial resistance (AMR) represents a pressing global health challenge with implications for developmental progress, as it increasingly manifests within pathogenic bacterial populations. This phenomenon leads to a substantial public health hazard, given its capacity to undermine the efficacy of medical interventions, thereby jeopardizing patient welfare. In recent years, an increasing number of machine learning methods have been employed to predict antimicrobial resistance. However, these methods still pose challenges in single-drug resistance prediction. This study proposed an effective model for predicting antimicrobial resistance to E. Coli by utilizing the eXtreme Gradient Boosting model (XGBoost), among ten other machine learning methods. The experimental results demonstrate that XGBoost outperforms other machine learning classification methods, particularly in terms of precision and hamming loss, with scores of 0.891 and 0.110, respectively. Our study explores the existing machine learning models for predicting antimicrobial resistance (AMR), thereby improving the diagnosis as well as treatment of infections in clinical settings.

Keywords

• antibiotics
• antimicrobial resistance
• <i>Escherichia coli</i>
• drug development
• medicinal chemistry
• multi-label classification