DnnARs: An Artificial Intelligence Technique for Prediction of Antimicrobial Resistant Strains in E. coli Bacteria Causing Urine Tract Infection

The study evaluates the predictive capabilities of machine learning and deep learning models in identifying antibiotic resistance patterns. It proposes a comprehensive framework that combines multiple algorithms and architectures. DnnARs, an artificial intelligence pipeline, is developed to identify antibiotic-resistant E. coli strains. The results show that deep learning models outperform ML models, with classification accuracy of 97%, specificity of 91%, and sensitivity of 100%. This research aims to improve diagnostic tools, medication management, and reduce antibiotic resistance, integrating deep learning models into clinical workflows.