Abstract Scope |
The primary objective of this study is to evaluate machine learning (ML) models in tandem with high-fidelity FEA data for the accelerated prediction of the biomechanical response in the acetabulum of the human hip joint, during the walking gait. The parameters used in the FEA study included the subject weight, number and distribution of fins on the shell, bone condition and phases of the gait. Such parametric variation involving 26 variables and a full factorial design resulted in a dataset of 10,752 datapoints. While utilising hyperparameter tuning, K-fold cross validation and statistical learning approaches, a number of ML models were trained on the FEA dataset, and the random forest model performed the best with a coefficient of determination (R2) value of 0.99/0.97 and Root Mean Square Error (RMSE) of 0.02/0.01 on the training/test dataset. Taken together, this study establishes the potential of ML approach as a fast surrogate of FEA. |