Identifying Key Determinants of HPV Vaccination Uptake: A National Health Survey Analysis to Inform Targeted Public Health Strategies

Background: Human papillomavirus (HPV) poses a significant threat to human health, but the factors influencing HPV vaccination remain unclear.This study applies machine learning and big data analytics to identify key determinants of HPV vaccination and provide data-driven evidence for the development of targeted public health strategies.Methods: Data from the National Health Interview Survey (NHIS) were analyzed using computational statistical methods, including 17,208 participants, divided into HPV-vaccinated and non-vaccinated groups.Weighted statistical tests and multivariate logistic regression were conducted in R, and the LASSO algorithm was applied for feature selection.The study emphasizes data preprocessing, feature engineering, and model validation in a large-scale health dataset.Results: Key factors identified include age, sex, race, education, region, cancer history, insurance status, and flu vaccination history.LASSO regression confirmed the robustness of these predictors in a high-dimensional dataset.The results highlight disparities that can be addressed through computational public health interventions.Conclusion: This study demonstrates the utility of machine learning in public health analytics for identifying vaccination barriers.The findings support the development of personalized, algorithm-driven vaccination campaigns and