Predictive Modeling of Orthopedic Surgical Outcomes in a Global Orthopedic Device Manufacturer’s Implant Procedures Using AdaBoost and Support Vector Regression

Abstract

Global Orthopedic Device Manufacturer’s is a leader in musculoskeletal health innovation through advanced orthopedic implant solutions. This study examines the relationships between implant size, patient age, bone density, surgical time, and functional outcomes in joint replacement procedures. Understanding these variables enables better surgical planning, outcome prediction, and personalized patient care optimization. This research improves predictive modeling in orthopedic surgery by using machine learning algorithms to predict surgical complexity and patient outcomes, enabling data-driven preoperative planning and improved resource allocation. AdaBoost regression and support vector regression algorithms were used to model the relationships between patient characteristics, implant specifications, and surgical outcomes through statistical analysis and correlation estimation. Both models demonstrated exceptional predictive accuracy (R²> 0.98). Bone density emerged as a strong predictor of functional scores (r=0.99), while patient age and implant size were associated with surgical duration. Machine learning algorithms effectively predict orthopedic surgical outcomes. Bone health significantly influences functional recovery, emphasizing the importance of preoperative assessment for surgical planning and patient-specific treatment optimization. Key words: Orthopedic implants, Adaboost regression, Support Vector regression, Bone density, Surgical prognosis, Functional outcomes