Effects of Different Vaccines on Genetic Immune and Antibody Response Using Machine Learning methods

Machine learning models have shown promising results in analyzing genetic, immune, and antibody responses to vaccines. Studies have identified certain genes consistently activated in individuals with a robust immune response to the flu vaccine, and specific antibodies most effective in preventing infection. In a recent study using a dataset of 161 blood samples from individuals who received the BNT162b2 and ChAdOx1 vaccines, machine learning algorithms accurately predicted vaccine efficacy based on genetic, immune, and antibody responses. The AdaBoost model had the highest accuracy at 98.9%, followed by the Decision Tree model at 93.8%, and k-Nearest Neighbors at 87.7%. The AdaBoost model also performed the best overall, with the highest precision and recall for both vaccines.