Data imbalance can lead to several issues in epidemiological research. For one, it can bias the results of predictive models, making them less reliable. When a dataset is imbalanced, models may become overfitted to the majority class, failing to accurately predict the minority class. This can be particularly problematic in disease prediction and control, where accurate identification of cases is crucial.
