Class imbalance poses a significant challenge in epidemiological studies because most classification algorithms are biased towards the majority class. This bias can lead to poor predictive performance, especially in identifying rare diseases or conditions. For example, if we are trying to predict a rare disease outbreak, a model trained on an imbalanced dataset may fail to identify true positive cases, leading to inaccurate predictions and potentially harmful public health decisions.
