Blood Biomarkers for Knee Osteoarthritis using a Machine Learning Model with an Emphasis on Adult Patient

Knee Osteoarthritis (OA) is a chronic joint disease that reduces mobility and quality of life. Diagnostic procedures generally rely on imaging techniques, which frequently diagnose OA in advanced stages, restricting early management. The purpose of this study was to use machine learning (ML) techniques to find prospective blood biomarkers for the early identification of adult patients’ knee osteoarthritis (KOA). A strong prediction model was created by analysing a dataset of 2000 records. Machine learning to detect early OA using blood-based biomarkers such as CRP, IL-6, TNF-α, COMP and MMP-3. OA detection through Machine Learning Techniques. Normalization and outlier detection were used to pre-process biomarker data from a cohort of participants, which were then selected using correlation analysis and Principal Component Analysis (PCA). A Random Forest model was created, with an AUC-ROC of 0.92, Sensitivity of 0.85 and Specificity of 0.90, CRP and COMP emerged as major predictors of OA. These findings imply that combining machine learning with biomarker analysis provides a non-invasive, accurate and early diagnosis strategy, allowing for faster clinical interventions and improved patient outcomes.