A Neutrosophic Random Forest Approach for Preeclamptic Risk Prediction with Uncertainty Quantification
This study presents a novel integration of Random Forest with neutrosophic logic to improve preeclampsia risk prediction while quantifying prediction uncertainty. Using clinical data from 352 patients, the model achieved 72.73% accuracy with high sensitivity (0.898) in identifying control cases, tho...
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| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
University of New Mexico
2025-10-01
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| Series: | Neutrosophic Sets and Systems |
| Assuntos: | |
| Acceso en liña: | https://fs.unm.edu/NSS/10.NeutrosophicRandomForest.pdf |
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