In Epidemiology, datasets often exhibit class imbalance, especially when studying rare diseases or public health conditions that occur infrequently. For example, while studying the occurrence of a rare infectious disease, the number of affected individuals might be much smaller than the number of unaffected individuals. This imbalance can skew the results of predictive models, making it difficult to accurately identify and predict disease instances. ADASYN helps mitigate this issue by creating synthetic examples of the minority class, leading to more reliable and valid predictive analytics.
