A 2025 study by Eisa and colleagues introduces an innovative approach that combines a seagull-inspired optimization algorithm with a random forest classifier. By smartly selecting vital genes, the method boosts breast cancer detection accuracy and may reshape diagnostic protocols through streamlined analysis.
Q&A
- What is the Seagull Optimization Algorithm?
- How does random forest contribute to this study?
- Why is 22-gene selection significant?