Enhancing Antimicrobial Resistance Detection in Pneumonia

Antimicrobial Resistance, AMR, is a global health challenge complicating the treatment of bacterial infections such as pneumonia. This paper will focus on the development of a machine learning-based framework for the classification of AMR in pneumonia using a dataset inspired by bacterial genomic and phenotypic features. Some key algorithms, such as ExtraTrees, LightGBM, and Random Forest, have been used to build the predictive models that can identify resistant bacterial strains. The ExtraTrees algorithm had the highest accuracy of 97.08%, which shows that it is efficient in handling high-dimensional datasets and capturing feature interactions. LightGBM was close to this with an accuracy of 96.63 %, showing superiority in speed and computational efficiency, while Random Forest had an accuracy of 96.59 %, which showed greater stability and interpretability in noisy datasets. This study demonstrated the potential capability of machine learning models to realize early diagnosis with targeted treatment, thus improving outcomes for patients' clinical care by extensive exploratory data analysis and rigorous model evaluation.