Abstract The rapid deployment of COVID-19 vaccines has necessitated the ongoing surveillance of adverse events, with abnormal uterine bleeding (AUB) emerging as a reported concern in vaccinated females. We aimed to develop a machine learning (ML) model to predict post-vaccination AUB in women aged less than 50 years. A large-scale national cohort, the Korean Nationwide Cohort (K-COV-N cohort), was utilized, comprising over 7 million participants. The study employed advanced ML techniques, including ensemble models combining gradient boosting machine and logistic regression, and conducted feature importance analysis. The dataset was meticulously curated, focusing on relevant demographics and variables, and balanced using Synthetic Minority Over-sampling Technique. Using a national cohort of over 2 million COVID-19 vaccinated cases in South Korea, we developed a ML model for AUB prediction. Our study is […]
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