Interpretable Machine Learning-Based Differential Diagnosis of Hip and Knee Osteoarthritis Using Routine Preoperative Clinical and Laboratory Data

Baigarayeva Z. Imanbek B. Boltaboyeva A. Amangeldy B. Tasmurzayev N. Ozhikenov K. Baimbetov D. Beisembekova R. Maeda-Nishino N.
January 2026 Multidisciplinary Digital Publishing Institute (MDPI)

Algorithms
2026 #19 Issue 1

Osteoarthritis (OA) of the hip (coxarthrosis) and knee (gonarthrosis) is a leading cause of disability worldwide. Differential diagnosis typically relies on imaging modalities such as X-rays and Magnetic Resonance Imaging (MRI). However, advanced imaging can be expensive and inaccessible, highlighting the need for non-invasive diagnostic tools. This study aimed to develop and validate an interpretable machine learning model to distinguish between hip and knee osteoarthritis using standard preoperative clinical and laboratory data. This model is designed to assist physicians in prioritizing whether to order a hip or a knee X-ray first, thereby saving time and medical resources. The study utilized retrospective data from 1792 patients treated at the City Clinical Hospital in Almaty, Kazakhstan. After applying inclusion and exclusion criteria, five machine learning algorithms were used for training and evaluation: Decision Tree, Random Forest, Logistic Regression, XGBoost, and CatBoost. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were employed to interpret predictions and determine the contribution of each feature. The XGBoost model demonstrated the best performance, achieving an accuracy of 93.85%, a precision of 95.15%, a recall of 90.51%, and an F1-score of 92.41%. SHAP analysis revealed that age, glucose and leukocyte levels, urea, and BMI made the greatest contributions to the model’s predictions, while local analysis using LIME indicated that age, leukocyte levels, glucose, erythrocytes, and platelets were the most influential features. These findings support the use of machine learning for cost-effective early osteoarthritis triage using routine preoperative data.

clinical data , coxarthrosis , differential diagnosis , gonarthrosis , laboratory data , LIME , machine learning , osteoarthritis , SHAP

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Institute of Automation and Information Technology, Satbayev University, Almaty, 050013, Kazakhstan
Faculty of Information Technologies and Artificial Intelligence, Al Farabi Kazakh National University, Almaty, 050040, Kazakhstan
LLP “Kazakhstan R&D Solutions”, Almaty, 050056, Kazakhstan
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, 94305, CA, United States
HAKUAI Medical Corporation, Osaka, 573-1010, Japan

Institute of Automation and Information Technology
Faculty of Information Technologies and Artificial Intelligence
LLP “Kazakhstan R&D Solutions”
Department of Psychiatry and Behavioral Sciences
HAKUAI Medical Corporation

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