{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T22:28:23Z","timestamp":1768948103213,"version":"3.49.0"},"reference-count":60,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["BR24992820"],"award-info":[{"award-number":["BR24992820"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Background: Osteoarthritis (OA) is a prevalent chronic degenerative disorder, with coxarthrosis (hip OA) and gonarthrosis (knee OA) representing its most significant clinical manifestations. While diagnosis typically relies on imaging, such methods can be resource-intensive and insensitive to early disease trajectories. Objective: This study aims to achieve the differential diagnosis of coxarthrosis and gonarthrosis using solely routine preoperative clinical and laboratory data, benchmarking state-of-the-art machine learning algorithms. Methods: A retrospective analysis was conducted on 893 patients (617 with knee OA, 276 with hip OA) from a clinical hospital in Almaty, Kazakhstan. The study evaluated a diverse portfolio of models, including gradient boosting decision trees (LightGBM, XGBoost, CatBoost), deep learning architectures (RealMLP, TabDPT, TabM), and the pretrained tabular foundation model RealTabPFN v2.5. Results: The RealTabPFN v2.5 (Tuned) model achieved superior performance, recording a mean ROC\u2013AUC of 0.9831, accuracy of 0.9485, and an F1-score of 0.9474. SHAP interpretability analysis identified heart rate (66.2%) and age (18.1%) as the dominant predictors driving the model\u2019s decision-making process. Conclusion: Pretrained tabular foundation models demonstrate exceptional capability in distinguishing OA subtypes using limited clinical datasets, outperforming traditional ensemble methods. This approach offers a practical, high-performance triage tool for primary clinical assessment in resource-constrained settings.<\/jats:p>","DOI":"10.3390\/a19010087","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T09:24:34Z","timestamp":1768901074000},"page":"87","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Non-Imaging Differential Diagnosis of Lower Limb Osteoarthritis: An Interpretable Machine Learning Framework"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1919-3570","authenticated-orcid":false,"given":"Zhanel","family":"Baigarayeva","sequence":"first","affiliation":[{"name":"Institute of Automation and Information Technology, Satbayev University, Almaty 050013, Kazakhstan"},{"name":"Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"},{"name":"LLP Kazakhstan R&D Solutions, Almaty 050056, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Assiya","family":"Boltaboyeva","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technology, Satbayev University, Almaty 050013, Kazakhstan"},{"name":"Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"},{"name":"LLP Kazakhstan R&D Solutions, Almaty 050056, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baglan","family":"Imanbek","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4089-6337","authenticated-orcid":false,"given":"Bibars","family":"Amangeldy","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3039-6715","authenticated-orcid":false,"given":"Nurdaulet","family":"Tasmurzayev","sequence":"additional","affiliation":[{"name":"Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kassymbek","family":"Ozhikenov","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technology, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Assylbek","family":"Ozhiken","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technology, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2628-2515","authenticated-orcid":false,"given":"Zhadyra","family":"Alimbayeva","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technology, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8525-8030","authenticated-orcid":false,"given":"Naoya","family":"Maeda-Nishino","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA 94305, USA"},{"name":"HAKUAI Medical Corporation, Osaka 573-1010, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"ref_1","unstructured":"Sen, R., and Hurley, J.A. 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