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...
I tiakina i:
| Ngā kaituhi matua: | , , , , |
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| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
University of New Mexico
2025-10-01
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| Rangatū: | Neutrosophic Sets and Systems |
| Ngā marau: | |
| Urunga tuihono: | https://fs.unm.edu/NSS/10.NeutrosophicRandomForest.pdf |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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