New Publication: Feature reduction using swarm optimization and random forest classifiers for early diabetes risk prediction

We’re excited to announce our latest research has been accepted for publication in Scientific Reports (Nature Portfolio)!

This work presents a novel approach to early diabetes risk prediction by combining swarm intelligence-based feature selection with Random Forest classification. The study demonstrates that leveraging swarm optimization to reduce dimensionality can maintain — or even improve — predictive performance while significantly lowering computational complexity.