Predicting the distribution patterns of antibiotic-resistant microorganisms in the context of Jordanian cases using machine learning techniques

Antimicrobial resistance (AMR) is the fourth leading cause of mortality in Jordan, but there is limited data on demographics and clinical characteristics. A retrospective analysis of microbiology records at Al-Hussein/Salt Hospital revealed Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus as the most commonly isolated microorganisms. The RF model showed superior accuracy in forecasting AMR patterns, highlighting the importance of monitoring AMR to ensure appropriate antibiotic therapy.