Early recurrence prediction model for DLBCL based on Gaussian mixture model bidirectional clustering resampling and improved deep forest

Key Points

• The model significantly improves early recurrence predictions for DLBCL, which is critical for treatment planning.
• Key metrics show that the Gaussian mixture model enhances prediction accuracy by effectively clustering patient data.
• Evaluation utilized a deep forest approach alongside bidirectional clustering for improved predictive performance.
• The findings may enable more personalized treatment strategies, but further validation with larger datasets is essential.