Classification of an Individual's Vaccination Status Using Ensemble Hard Voting Classifier

Abstract

Vaccination is a proactive medical immunization procedure where an inactivated form of a disease-causing agent (such as a virus) is administered to boost the body's defense systems. Efficient management of vaccination status is crucial in healthcare management, disease eradication, community immunity ("herd immunity"), disease prevention, and global health security. Ensuring precise monitoring and validation of an individual's vaccination status is indispensable, especially in the context of emerging diseases and epidemics. This study evaluates the likelihood of individuals obtaining vaccination for the H1N1 virus and the seasonal flu vaccine. Ensemble methods combine the predictions of multiple base classifiers to enhance overall performance. One such method, the hard voting classifier, aggregates the votes from each base classifier and selects the class with the majority vote as the final prediction. This approach leverages the strengths of different classifiers, reducing the risk of individual model biases and improving generalization using metrics such as precision, recall, accuracy, and F1-score are employed to assess the system's effectiveness. The results demonstrate how data-driven methods can address population wellness and improve vaccination rates using an ensemble method. The proposed ensemble hard voting classifier achieved accuracies of 0.905 and 0.907 on the H1N1 and seasonal vaccine datasets, respectively. Using an ensemble approach like the hard voting classifier enhances prediction accuracy and robustness, ultimately leading to better decision making in public health initiatives.