{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T19:48:10Z","timestamp":1791229690521,"version":"4.1.0"},"reference-count":33,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Antibiotics"],"abstract":"<jats:p>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\u20132025) 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 validation methods. Results: Among the three machine learning models tested, Random Forest (RF) showed the best performance in predicting antibiotic resistance of E. coli, achieving median accuracy, precision, recall, and F1-scores of 0.90 and AUC values up to 0.99 for key antibiotics. CatBoost performed similarly but was less stable with imbalanced data, while Naive Bayes showed lower accuracy. Feature importance analysis highlighted strong inter-antibiotic resistance links, especially among \u03b2-lactams, and some influence of demographic factors. Conclusions: This study highlights the potential of simple, high-performing models using structured clinical data to predict antimicrobial resistance, especially in resource-limited clinical settings. By incorporating machine learning into antimicrobial resistance (AMR) surveillance systems, our goal is to support the advancement of rapid diagnostics and targeted antimicrobial stewardship approaches, which are essential in addressing the growing challenge of multidrug resistance.<\/jats:p>","DOI":"10.3390\/antibiotics14100969","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T09:35:49Z","timestamp":1758879349000},"page":"969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Machine Learning Model for Predicting Multidrug Resistance in Clinical Escherichia coli Isolates: A Retrospective General Surgery Study"],"prefix":"10.3390","volume":"14","author":[{"given":"H\u00fcseyin Kerem","family":"Tolan","sequence":"first","affiliation":[{"name":"Department of General Surgery, \u00dcmraniye Training and Research Hospital, University of Health Sciences, 34668 Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5488-2262","authenticated-orcid":false,"given":"\u0130rfan","family":"Ayd\u0131n","sequence":"additional","affiliation":[{"name":"Pharmacy Services Program, Pharmacy Services Department, Health Services Vocational School, Fenerbah\u00e7e University, 34758 Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5231-2768","authenticated-orcid":false,"given":"Handan","family":"Tanyildizi-Kokkulunk","sequence":"additional","affiliation":[{"name":"Department of Arts & Sciences, Maine Maritime Academy, Castine, ME 04420, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9460-1215","authenticated-orcid":false,"given":"Mehmet","family":"Karaku\u015f","sequence":"additional","affiliation":[{"name":"Department of Medical Microbiology, Hamidiye Faculty of Medicine, University of Health Sciences, 34668 Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Y\u00fcksel","family":"Akkaya","sequence":"additional","affiliation":[{"name":"Department of Medical Microbiology, Hamidiye Faculty of Medicine, University of Health Sciences, 34668 Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4673-7982","authenticated-orcid":false,"given":"Osman","family":"Kaya","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, G\u00f6ztepe Prof. Dr. S\u00fcleyman Yal\u00e7\u0131n Training and Research Hospital, 34730 Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ferruh Kemal","family":"\u0130\u015fman","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, G\u00f6ztepe Prof. Dr. S\u00fcleyman Yal\u00e7\u0131n Training and Research Hospital, 34730 Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nasrollahian, S., Graham, J.P., and Halaji, M. 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