## 4 Discussion
### Clinical Implications of Learning-Curve Behaviors in ML Models
#### 4.1.1 Analysis of Problematic Learning-Curve Behaviors
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Learning-Curve Behavior** & **Potential Cause** & **Clinical Implication** \\ \hline Training and validation curves converge and plateau, but the validation curve is higher than the training curve. & Data leakage or overly simplistic training data. Improper regularization. & Indicates that the validation set is not representative of real-world data. Clinically, this could lead to overconfidence in the model’s performance, resulting in poor generalization to unseen patient data. \\ \hline Training and validation curves never converge. & Model underfitting due to insufficient complexity, poor feature representation, or inadequate training. & Suggests that the model fails to learn meaningful patterns. Clinically, this results in low diagnostic accuracy and unreliable predictions. \\ \hline Training curve plateaus, but the validation curve continues to improve. & Validation set may be easier or less diverse than the training set. & Indicates a mismatch between training and validation data. Clinically, this could lead to overestimation of the model’s generalizability. \\ \hline Training and validation curves converge but never plateau. & Model is overfitting to noise or artifacts in the data. & Suggests that the model is learning spurious patterns. Clinically, this could lead to unreliable predictions and increased false positives or negatives. \\ \hline Large gap between training and validation curves. & Overfitting due to excessive model complexity or insufficient regularization. & Indicates poor generalization. Clinically, this could result in high accuracy on training data but poor performance in real-world scenarios. \\ \hline Training and validation curves oscillate significantly. & High learning rate or unstable optimization process. & Suggests instability in training. Clinically, this could lead to unpredictable model behavior and unreliable predictions. \\ \hline Validation curve improves initially but then deteriorates. & Overfitting due to prolonged training or lack of early stopping. & Indicates that the model is memorizing the training data. Clinically, this could result in poor generalization and unreliable diagnostic outcomes. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Problematic learning (accuracy) curve behaviors, their potential causes, and clinical implications. performance, often indicate an unstable optimization process, potentially due to a high learning rate or poor initialization. Clinically, this instability translates to unpredictable model behavior, making it unsuitable for deployment in critical healthcare applications. Another problematic behavior is validation-curve deterioration, where the validation curve initially improves but later deteriorates. This is a clear sign of overfitting, typically occurring when training continues beyond the point of optimal generalization. Clinically, this could lead to a model that performs well during initial testing but fails to maintain accuracy over time, especially as patient populations or data distributions evolve. The clinical implications of these poor learning-curve behaviors are significant. Overfitting can lead to false positives, resulting in unnecessary treatments or interventions, while underfitting can result in missed diagnoses, delaying critical care for patients. Data leakage can create a false sense of confidence in the model's performance, leading to its premature deployment in clinical settings. To mitigate these risks, it is essential to monitor learning curves during training and address any anomalies promptly. Techniques such as cross-validation, early stopping, regularization, and robust data preprocessing can help ensure that the model learns meaningful patterns and generalizes well to unseen data. By understanding and addressing problematic learning-curve behaviors, researchers and clinicians can develop ML models that are not only statistically robust but also clinically reliable.