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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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Hoofdauteurs: Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Lorenzo Cevallos-Torres, Leonel Vasquez-Cevallos, Dayron Rumbaut-Rangel
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: University of New Mexico 2025-10-01
Reeks:Neutrosophic Sets and Systems
Onderwerpen:
preeclampsia
neutrosophic logic
random forest
uncertainty quantification
maternal health
risk prediction
Online toegang:https://fs.unm.edu/NSS/10.NeutrosophicRandomForest.pdf
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