Classification of Orthopedic Diseases Using Biomechanical Properties with Machine Learning Methods

Orthopedic diseases significantly affect the musculoskeletal system, reducing patients’ functional capacity and quality of life. Accurate and early classification of such conditions is therefore critical for effective clinical decision-making. This study proposes a machine learning-based framework for orthopedic disease classification using biomechanical features, with a particular emphasis on handling class imbalance. A set of classification algorithms, including Random Forest, Support Vector Machine, Naive Bayes, Logistic Regression, XGBoost, LightGBM, and a Soft Voting Ensemble (XGBoost + LightGBM), were evaluated on a dataset of 310 patients using 10-fold cross-validation. The impact of the Synthetic Minority Over-sampling Technique (SMOTE) was systematically analyzed by comparing model performance with and without its application. Evaluation metrics included Accuracy, Precision, Recall, F1-score, and macro-average ROC-AUC. Results indicate that addressing class imbalance significantly improves model performance, particularly in terms of ROC-AUC. Among the tested methods, Logistic Regression demonstrated the most stable and competitive results. The best performance was achieved by Logistic Regression with SMOTE, yielding 87% accuracy and a macro-average ROC-AUC of 0.96. These findings highlight the importance of imbalance-aware modeling strategies in orthopedic disease classification.