Machine Learning Model for Predicting Multidrug Resistance in Clinical <i>Escherichia coli</i> Isolates: A Retrospective General Surgery Study.

ABSTRACT: Background/Objectives: Escherichia coli is one of the leading causes of surgical site infections (SSIs) and poses a growing public health concern due to its increasing antimicrobial resistance. High rates of extended-spectrum beta-lactamase (ESBL) production among E. coli strains complicate treatment outcomes and emphasize the need for effective surveillance and control strategies. Methods: A total of 691 E. coli isolates from general surgery clinics (2020-2025) were identified using MALDI-TOF MS. Antibiotic susceptibility data and patient variables were cleaned, encoded, and used to predict resistance using the Random Forest, CatBoost, and Naive Bayes algorithms. SMOTE addressed class imbalance, and model performance was assessed through various validati

SUBMITTER: Tolan HK 

PROVIDER: S-EPMC12561689 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature