I would consider using techniques like resampling (oversampling the minority class or undersampling the majority class), using algorithms designed for imbalanced data like the Synthetic Minority Oversampling Technique (SMOTE), adjusting class weights in the model, or using evaluation metrics like precision-recall or F1-score instead of accuracy to better reflect performance.