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...
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
University of New Mexico
2025-10-01
|
| Collection: | Neutrosophic Sets and Systems |
| Sujets: | |
| Accès en ligne: | https://fs.unm.edu/NSS/10.NeutrosophicRandomForest.pdf |
| Tags: |
Pas de tags, Soyez le premier à ajouter un tag!
|