An explainable ensemble machine learning framework for predicting cartilage biomechanical degradation from transcriptomic profiles: toward smart orthopedic device design

Introduction Osteoarthritis (OA) is a common joint disease characterized by progressive biomechanical deterioration of cartilage tissue. This study aims to develop an explainable ensemble machine learning model capable of predicting cartilage biomechanical deterioration from transcriptomic profiles and to present this model as a digital biomarker for intelligent orthopedic devices. Methods Seven independent datasets (n = 203) obtained from the Gene Expression Omnibus (GEO) database were harmonized using the Joint ComBat algorithm. Differentially expressed gene (DEG) analysis performed on the training cohort (n = 122) identified 93 significant genes. An ensemble model combining Random Forest (RF), XGBoost, and Support Vector Machine (SVM) algorithms was trained using these genes. The disease probability generated by the model was redefined as a continuous Cartilage Degradation Index (CDI). The model’s performance and generalizability were tested on four independent validation cohorts including patients undergoing arthroscopic partial meniscectomy (APM, n = 44), chondrocytes subjected to in vitro mechanical stress (n = 18), and different geographic cohorts (n = 19). The biological basis of the model’s decisions was elucidated using SHAP (SHapley Additive exPlanations) analysis and comprehensive pathway enrichment analyses. Results The ensemble model demonstrated a 5-fold cross-validation performance with an area under the curve (AUC) of 0.960. Permutation testing (p < 0.001) and bootstrap 95% confidence interval (CI) (AUC: 0.900–0.996) confirmed the model’s robustness. CDI modeled degradation across a biologically consistent spectrum: 0.03–0.09 in healthy cartilage, 0.30–0.42 in early-stage APM, and 0.79 in advanced-stage OA. CDI scores were significantly higher in cells exposed to abnormal mechanical stress (overstress) compared to the control group (0.60 vs. 0.52, AUC = 0.778). SHAP analysis highlighted COL15A1, CXCL14, CISH, FAP, and PIM2 as the most critical mechanosensitive genes. Pathway analysis confirmed that the model is based on mechanotransduction mechanisms such as focal adhesion, extracellular matrix (ECM) organization, and epithelial–mesenchymal transition. Conclusion This study presents a transcriptomic CDI capable of measuring cartilage biomechanical degradation at the molecular level. The identified mechanosensitive gene profile holds strong potential as a digital biomarker for next-generation smart orthopedic implants and wearable biomechanical sensors.