The primary issue with imbalanced data is that it can lead to misleading performance metrics. For example, a model might show high _accuracy_ by simply predicting the majority class. However, its ability to detect cancerous cases (minority class) may be very poor. This can be particularly problematic in _early detection_ and _diagnosis_, where identifying the minority class (cancer) accurately is crucial for effective treatment.
