The AUC of FRS and RPCE risk scores and ML models with feature selection based on a 30% validation dataset.

<p>The AUC of FRS and RPCE risk scores and ML models with feature selection based on a 30% validation dataset.</p>

Saved in:

Bibliographic Details
Main Author:	Sazzli Kasim (11218558) (author)
Other Authors:	Putri Nur Fatin Amir Rudin (17972745) (author), Sorayya Malek (9288581) (author), Nurulain Ibrahim (14247915) (author), Xue Ning Kiew (21560160) (author), Nafiza Mat Nasir (12035051) (author), Khairul Shafiq Ibrahim (11218552) (author), Raja Ezman Raja Shariff (11218555) (author)
Published:	2025
Subjects:	Microbiology Cell Biology Biotechnology Marine Biology Cancer Mathematical Sciences not elsewhere classified validate machine learning systolic blood pressure support vector machine shapley additive explanations net reclassification index framingham risk score ensure robust performance lr model achieved gradient boosting model generalized linear model year cvd risk western pacific region significant nri improvement several ml algorithms ensuring ongoing validation best individual model western pacific performing model model evaluation ensemble model xlink "> web platform waist circumference time use test ratio study showed study aimed similar genetic significantly exceed rediscover registry primarily based predict 10 nb ). naive bayes ml approaches malaysian cohort including rf highest auc glm ), gbm ). fold cross findings underscore could serve auc ), asian populations additional metrics 74 ). 688 participants 30 train
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items