## 3 Results
### Confusion Matrices  
A confusion matrix is a diagnostic tool that evaluates classification models in clinical settings by cross-tabulating predicted outcomes against actual results across four categories: true positives (correctly identified pathologies), true negatives (accurately detected normal cases), false positives (normal scans misclassified as abnormal), and false negatives (pathologies missed by the model). In clinical applications like retinal OCT analysis, confusion matrices objectively quantify a model's capacity to distinguish between diseases such as age-related macular degeneration and healthy scans, providing clinicians with a clear visual summary of correct diagnoses versus critical classification errors.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **AUC** & **Recall** & **Prec.** & **F1** & **Kappa** & **MCC** & **Specificity** & **Score** \\ \hline lr & 0.9423 * & 0.9832 * & 0.9409 & 0.9725 & 0.9156 * & 0.8660 * & 0.8674 * & 0.946 * & 0.964 * \\ \hline lightgbm & 0.9394 & 0.9827 & 0.9337 & 0.8886 * & 0.9106 & 0.8648 & 0.8654 & 0.942 & 0.960 \\ \hline rf & 0.9317 & 0.9794 & 0.9103 & 0.8862 & 0.8980 & 0.8467 & 0.8469 & 0.942 & 0.951 \\ \hline et & 0.9280 & 0.9786 & 0.9041 & 0.8812 & 0.8925 & 0.8384 & 0.8385 & 0.940 & 0.949 \\ \hline svm & 0.9279 & 0.9811 & 0.9290 & 0.8679 & 0.8955 & 0.8407 & 0.8441 & 0.927 & 0.955 \\ \hline ridge & 0.9249 & 0.9801 & 0.9614 & 0.8359 & 0.8942 & 0.8364 & 0.8413 & 0.907 & 0.960 \\ \hline lda & 0.9249 & 0.9801 & 0.9614 & 0.8359 & 0.8942 & 0.8364 & 0.8413 & 0.907 & 0.960 \\ \hline gbc & 0.9247 & 0.9757 & 0.9312 & 0.8542 & 0.8910 & 0.8337 & 0.8355 & 0.922 & 0.952 \\ \hline ada & 0.9058 & 0.9640 & 0.8831 & 0.8401 & 0.8610 & 0.7898 & 0.7904 & 0.894 & 0.926 \\ \hline dt & 0.8582 & 0.8445 & 0.8041 & 0.7754 & 0.  
The confusion matrices presented in Figure 2 reveal important insights about how SMOTE optimization affects ML algorithm performance when dealing with imbalanced data. Without SMOTE, the algorithm shows a concerning 7.34% rate of \"false-abnormal\" classifications, i.e., normal cases incorrectly classified as abnormal (false positives). At the same time, it produces \"false-normal\" classifications (i.e., abnormal cases incorrectly identified as normal, or false negatives) at a rate of 5.02%. The ability to correctly identify abnormal cases sits at 94.98% (true-positive rate), while correct normal identifications occur 92.66% of the time (true-negative rate). When SMOTE is applied, a significant shift is observed in these patterns. The \"false-abnormal\" rate decreases substantially to 5.39%, representing improved specificity and fewer normal cases being incorrectly flagged. This comes with a trade-off, however, as the \"false-normal\" rate increases slightly to 5.94%. Correct identification rates adjust accordingly, with abnormal case detection (sensitivity) declining marginally to 94.06%, while normal case accuracy improves to 94.61%.  
This transformation occurs because SMOTE creates synthetic samples of the minority class, giving the algorithm more balanced training data. The result is a classification approach that shows improved performance in correctly identifying normal cases at the expense of a slight decrease in abnormal case detection. The fundamental trade-off revealed in these matrices is that SMOTE helps reduce the potentially disruptive \"false-abnormal\" errors (false positives) at the expense of a slight increase in \"false-normal\" classifications (false negatives).  
For many applications where minimizing false alarms is important for operational efficiency, this recalibration represents a worthwhile improvement, though practitioners must carefully consider whether the slight decrease in abnormal detection sensitivity is acceptable for their specific use case. In scenarios where missing an abnormal case carries significant consequences, alternative approaches might be preferable.