Predicting immunogenicity of COVID-19 vaccines in hemodialysis patients with renal disease

Individuals who are diagnosed with chronic kidney disease, particularly those receiving maintenance hemodialysis treatment, face a greater likelihood of suffering from severe symptoms and fatality due to COVID-19. This study aimed to explore the optimal vaccination approach for these individuals. The study used data analysis tasks such as data preprocessing, cleaning, and exploration, and machine learning models including linear regression, random forest, XGBoost, gradient boosting, AdaBoost, decision trees, Lasso, and ridge regression were used to construct the predictive model. The study found that the Lasso model performed the best overall in predicting anti-S IgG antibodies levels in response to COVID-19 vaccines for people with kidney failure with MAE of 8.81, RMSE of 19.59, and R2 value of 0.93. The adjusted R2 value for the Lasso model was also 0.93, indicating that the model's ability to explain the variance in the data was not affected by the number of predictors in the model. The Random Forest model best predicted the duration of immunogenicity, with R2 and adjusted R2 values of 0.71 and 0.69, respectively. The ensemble model that includes all eight models, i.e., Ridge, Lasso, Linear Regression, Random Forest, AdaBoost, Gradient Boosting, XGBoost, and Decision Tree, has the best performance with the lowest MAE, the lowest RMSE, the highest R2, and the highest adjusted R2 values of 3.91, 5.00, 0.73, and 0.72, respectively. However, further research is required to validate these models and extend their application to different populations and vaccine types, as well as considering other factors that may affect immune response to COVID-19 vaccines. These findings can be helpful in improving vaccination strategies and promoting public health.