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...

詳細記述

書誌詳細

主要な著者:	Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Lorenzo Cevallos-Torres, Leonel Vasquez-Cevallos, Dayron Rumbaut-Rangel
フォーマット:	Artigo
言語:	Inglês
出版事項:	University of New Mexico 2025-10-01
シリーズ:	Neutrosophic Sets and Systems
主題:	preeclampsia neutrosophic logic random forest uncertainty quantification maternal health risk prediction
オンライン･アクセス:	https://fs.unm.edu/NSS/10.NeutrosophicRandomForest.pdf
タグ:	タグ追加 タグなし, このレコードへの初めてのタグを付けませんか!