Unraveling Antibiotic
Resistance in Escherichia coli: A Genomic Prediction Breakthrough
Phulre Ajay Kumar and Tiwari Kovid
Res. J. Biotech.; Vol. 20(10); 255-261;
doi: https://doi.org/10.25303/2010rjbt2550261; (2025)
Abstract
Escherichia coli has reached the point of antibiotic resistance, becoming a serious
public health problem that renders treatments ineffective and can result in infections
that are not treatable. Targeted therapy and reduced misuse of antibiotics could
be hastened by early aggression of resistance patterns. In this work, we investigate
the machine learning models that predict antibiotic resistance of the E. coli bacteria
at the genomic and phenotypic levels. We used several machine learning algorithms.
We evaluated the performance with accuracy, precision, recall and f1 score. Although
numerous studies operate in this domain, our results indicate that XGBoost achieved
the highest accuracy of 92.1%. The main novelty of our research is the feature selection
strategy, optimization techniques of the model as well as the combination of multiple
data to improve predictive performance.
Unlike traditional statistical approaches, our method leverages advanced machine
learning techniques to identify key resistance patterns effectively. The findings
suggest that machine learning can serve as a reliable tool for predicting antibiotic
resistance in clinical settings, helping to improve treatment decisions. Future
work can focus on expanding the dataset and incorporating explainable AI techniques
to enhance model interpretability.
