Update README.md

Author: SimonYip22

README.md

@@ -108,7 +108,6 @@ LightGBM Model (v1)
 
  # Model Comparison: LightGBM vs Neural Network (V1 & V2)
 
-```text
 | Aspect | LightGBM (V1) | Temporal Convolutional Network (TCN) (V2) |
 |--------|-------------------|-------------------|
 | **ML Model Name / Type** | LightGBM (Gradient Boosted Decision Trees) | Temporal Convolutional Network (TCN)(Neural network) |
@@ -120,5 +119,4 @@ LightGBM Model (v1)
 | **Strengths** | - Handles missing values gracefully.<br>- Fast training and inference.<br>- Provides feature importances.<br>- Works well with tabular summary features. | - Models temporal trends and interactions.<br>- Can capture subtle patterns in sequences of vitals.<br>- Potentially better performance on real-time deterioration prediction. |
 | **Weaknesses / Limitations** | - Ignores sequence and timing of events.<br>- May lose some granularity of patient trajectory.<br>- Cannot capture interactions over time. | - Requires more computation and tuning.<br>- Harder to interpret.<br>- Sensitive to missing data; requires careful imputation or masking. |
 | **Output** | Predictions per patient, feature importances, evaluation metrics (AUROC, PR-AUC, etc.) | Predictions per timestamp or per patient trajectory, evaluation metrics (AUROC, PR-AUC, potentially time-dependent metrics) |
-| **Use case / Deployment** | Baseline model; interpretable; fast deployment; can be used for early warning systems using summary features | Advanced model for final deployment or v2 experimentation; may be integrated in real-time monitoring dashboards for continuous deterioration prediction |
-```
+| **Use case / Deployment** | Baseline model; interpretable; fast deployment; can be used for early warning systems using summary features | Advanced model for final deployment or v2 experimentation; may be integrated in real-time monitoring dashboards for continuous deterioration prediction |