A Differential Evolution-Based Optimized Ensemble for Balanced and Imbalanced Medical Datasets

Key Points

• Improvements in minority class detection indicate a significant advancement in imbalanced datasets.
• The OEDE framework offers robust design features with effective performance on healthcare risk prediction tasks.
• Differential evolution methods optimize ensemble techniques, enhancing accuracy in identifying minority class groups.
• This approach highlights the potential for tailored solutions to address challenges in data imbalances.