In cancer research, datasets frequently have a disproportionate number of negative (non-cancerous) samples compared to positive (cancerous) samples. This imbalance can lead to machine learning models that are biased towards the majority class, resulting in poor performance in identifying cancer cases. By using SMOTE, researchers can create synthetic samples of cancer cases, which helps in building more robust and accurate predictive models.
