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 One of the primary challenges we encountered was a significant class imbalance, with a higher number of patients withdrawing from treatment compared to those staying.
 
 To address this issue, we implemented four different training approaches or pipelines on both the pre-pandemic and post-pandemic training datasets:
-1. **Using the Original Dataset**: The models were trained on the original datasets.
-2. **Class Weight Adjustment**: The models were trained on the original datasets but were penalized more heavily for misclassifying the minority class.
-3. **Oversampling**: Additional samples were generated for the minority class (patients staying) to balance the dataset.
-4. **Undersampling**: Samples from the majority class (patients withdrawing) were reduced to achieve balance.
+1. **Using the Original Dataset (ORIG)**: The models were trained on the original datasets.
+2. **Class Weight Adjustment (ORIG_CW)**: The models were trained on the original datasets but were penalized more heavily for misclassifying the minority class.
+3. **Oversampling (OVER)**: Additional samples were generated for the minority class (patients staying) to balance the dataset.
+4. **Undersampling (UNDER)**: Samples from the majority class (patients withdrawing) were reduced to achieve balance.
 
 These approaches resulted in multiple training datasets. However, to ensure a fair comparison of the models' performance across different pipelines, we utilized a common test dataset for evaluation, irrespective of the training approach followed.