Implanted Knee Kinematics Prediction: comparative performance analysis of machine learning techniques

Knee implantation is a popular knee surgery to replace damaged knee joint in Total knee arthroplasty (TKA). It is essential to predict postoperative knee kinematic before the surgery for patient-specific TKA surgical planning because outcome of the TKA operation strongly depends on types of prosthesis and surgical methods. Previously, we proposed postoperative kinematics (A-P and i-e patterns) prediction method based on generalized linear regression (GLR). However, this study mainly focuses on comparative performance analysis of the two popular machine learning methods (SVR and NN) in predictive model construction for postoperative kinematics prediction using PCA-based feature extraction, then compared with GLR method. It was found that predictive model's prediction performance slightly varies from each other's because the characteristics features of the kinematic patterns differs from each type. Therefore, this study recommends the best ML method (NN for A-P pattern and GLM for i-e pattern) with high prediction performance for predicting TKA outcome.