Feature-based ensemble modeling for addressing diabetes data imbalance using the SMOTE, RUS, and random forest methods: a prediction study
Purpose This study developed and evaluated a feature-based ensemble model integrating the synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) methods with a random forest approach to address class imbalance in machine learning for early diabetes detection, aiming to impr...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Ewha Womans University College of Medicine
2025-04-01
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| Series: | The Ewha Medical Journal |
| Subjects: | |
| Online Access: | http://www.e-emj.org/upload/pdf/emj-2025-00353.pdf |
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