Table 13 Comparison of parallel model integration with other techniques.
Aspect | Parallel model integration | Feature fusion | Self-attention mechanism |
|---|---|---|---|
Computational Cost | Very High (double models) | Moderate (one pass, feature-level fusion) | High (attention computation on features) |
Memory Usage | Very High | Moderate | High (attention matrices consume memory) |
Training/Inference Time | Long | Medium | Medium-High |
Risk of Overfitting | Medium (redundant features) | High (if fusion poorly handled) | Lower (attention focuses on key areas) |
Interpretability | Good (separate paths) | Lower (mixed feature space) | Best (attention weights are explainable) |
Scalability to New Data | Medium (needs both models tuned) | Medium | Better (flexible attention layer tuning) |
Suitability for Clinical Use | Cautiously Good (very accurate but heavy) | Good (lighter models possible) | Good (accurate and interpretable) |
