## 3 Results
### Comparative Model Performance  
The systematic evaluation of 14 machine learning models (Table 1) demonstrated significant variability in classification performance for retinal OCT image analysis. Logistic Regression (LR) achieved the highest accuracy (0.9396), AUC (0.9832), and composite score (0.963) calculated through Equation (12)'s weighted combination of discrimination power and clinical metrics, outperforming computationally intensive architectures such as LightGBM (accuracy: 0.9394, score: 0.960) and Random Forest (accuracy: 0.9317, score: 0.951). Notably, simpler linear models (LR, Ridge Classifier, LDA) dominated the top rankings, collectively achieving mean accuracy and AUC values of 0.9344 and 0.9821, respectively, compared to 0.9201 and 0.9767 for tree-based ensembles. This suggests that discriminative pathological features in OCT scans may be linearly separable in the VGG16-derived biomarker space.  
While LR achieved the highest aggregate accuracy and AUC (Table 1), its performance remained highly competitive in metrics where other models marginally surpassed it. Notably, LightGBM exceeded LR in precision (0.8886 vs. 0.8799), while KNN demonstrated superior recall (0.9849 vs. 0.9464), suggesting minor trade-offs between specificity and sensitivity across model architectures. However, LR maintained the second-highest precision (0.8871) and third-highest recall (0.9460), demonstrating robust equilibrium in clinical metrics where specialized models achieved isolated advantages. This balanced performance profile positions LR as the optimal compromise between detection reliability and confirmation specificity for retinal OCT analysis. The narrow margins in secondary metrics, precision differing by 0.0087 and recall by 0.0385 between LR and respective leaders, further support the model's clinical viability, as practical screening systems prioritize consistent performance across all diagnostic parameters over isolated metric maximization.  
When examining model performance across pathological subtypes, we observe distinct patterns indicating why LR outperforms more complex architectures. For neovascular conditions like CNV, characterized by fluid accumulation and membrane formation, LR achieves 96.2% sensitivity compared to Random Forest's 93.7%, likely because linear decision boundaries effectively separate the high-dimensional VGG16 features representing fluid distribution patterns. Conversely, for DRUSEN cases, which present as hyperreflective deposits with more uniform distribution patterns, tree-based models like LightGBM (94.3% accuracy) perform similarly to LR (94.6%). The optimal performance of LR can be attributedto three complementary factors: (1) the VGG16 feature extractor already captures non-linear relationships in retinal layer morphology, providing LR with transformed features that become linearly separable; (2) fluid accumulation patterns in DME and CNV manifest as consistent intensity gradients in OCT images, creating feature vectors with strong linear correlations to pathological status; and (3) LR's inherent regularization prevents overfitting to training-specific artifacts that might occur with more complex models. This suggests that while deep learning extracts complex hierarchical features from raw OCT data, the final classification boundary between normal and pathological states remains relatively linear in this transformed feature space, allowing LR to establish optimal separating hyperplanes that generalize effectively across heterogeneous OCT presentations.