{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T12:06:52Z","timestamp":1770984412617,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T00:00:00Z","timestamp":1770940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives:\u00a0Klebsiella pneumoniae is an opportunistic pathogen increasingly resistant to carbapenems and broad-spectrum antibiotics, complicating timely infection management. In critical cases like septic shock, where initiating effective antibiotics within 3 h improves survival, culture-based resistance testing is often too slow. This study evaluates machine learning (ML) algorithms for faster antimicrobial resistance prediction than conventional methods. Methods: In this retrospective study, antibiogram results of 607 Klebsiella pneumoniae isolates collected between 2017 and 2024 were combined with demographic and clinical information of the patients from whom the isolates were obtained. Four different ML algorithms, namely Decision Tree (DT), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN) and Random Forest (RF), were applied to classify the resistance status for 22 antibiotics. Model performances were evaluated using accuracy, precision, recall, F-score, AUC and feature importance metrics. Results: The RF model showed the highest overall performance in accurately predicting resistance to 22 antibiotics, achieving an average AUC value of 0.96. In particular, it predicted resistance to treatment-critical antibiotics such as Ertapenem (100%), Imipenem (93%) and Meropenem (95%) with high accuracy. Conclusions: ML models, especially RF, offer a powerful tool for rapid antibiotic resistance prediction, supporting accurate empirical treatment decisions and antimicrobial stewardship.<\/jats:p>","DOI":"10.3390\/diagnostics16040555","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T10:52:21Z","timestamp":1770979941000},"page":"555","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Model for Predicting Multidrug Resistance in Clinical Klebsiella pneumoniae Isolates"],"prefix":"10.3390","volume":"16","author":[{"given":"Yuksel","family":"Akkaya","sequence":"first","affiliation":[{"name":"Department of Medical Microbiology, Hamidiye Faculty of Medicine, University of Health Sciences, Istanbul 34668, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5488-2262","authenticated-orcid":false,"given":"Irfan","family":"Aydin","sequence":"additional","affiliation":[{"name":"Pharmacy Services Program, Pharmacy Services Department, Health Services Vocational School, Fenerbahce University, Istanbul 34758, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Handan","family":"Tanyildizi-Kokkulunk","sequence":"additional","affiliation":[{"name":"Department of Arts & Sciences, Maine Maritime Academy, Castine, ME 04420, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayse","family":"Erturk","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Clinical Microbiology, Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Education and Research Hospital, Istanbul 34668, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0272-5131","authenticated-orcid":false,"given":"Ibrahim Halil","family":"Kilic","sequence":"additional","affiliation":[{"name":"Department of Biology, Faculty of Arts and Sciences, Gaziantep University, Gaziantep 27410, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"key":"ref_1","first-page":"DC14","article-title":"Multi drug resistant Klebsiella isolates in burn patients: A comparative study","volume":"9","author":"Perween","year":"2015","journal-title":"J. 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