Research on prediction of contact stress of acetabular lining based on principal component analysis and support vector regression

Abstract

In the "worst-case" selection of hip prosthesis wear, it is necessary to calculate the contact stress of the acetabular liner. However, there are various combinations of acetabular prostheses. If calculated one by one, it will cause a large workload, a repeated and tedious calculation problem. To solve this problem, a machine learning prediction method by combining principal component analysis and support vector regressions (PCA-SVR) was established. First, the finite element method is used to analyze and calculate the contact stress of the acetabular liner in a typical combination to form a basic data set with the key size of the acetabular prosthesis as input and the contact stress as output; then, based on this data set, the PCA reduces the dimension of the input to obtain a new data set. Finally, based on this data set, SVR is used to establish the mapping model, and the optimal value of the model parameter C and is obtained by combining K-fold cross-validation and grid search method. The maximum absolute error of the prediction on the test data set is only 0.1986, the root mean square error RMSE is only 0.09309, and the R² value is 0.9426, which verifies the effectiveness of the prediction model. At the same time, the prediction performance is compared with the Ridge regression and Lasso models, which further verifies the superiority of the proposed method.

Keywords

• worst-case
• acetabular prosthesis
• pca-svr
• contact stress
• prediction model