In epidemiology, researchers often deal with rare diseases or conditions that have a low prevalence in the population. Traditional machine learning algorithms may underperform on such imbalanced datasets, leading to biased predictions. By using SMOTE, epidemiologists can create a more balanced dataset, improving the performance of predictive models and ensuring that rare events are adequately represented.
