FETAL HEALTH RISK STATUS IDENTIFICATION SYSTEM BASED ON CARDIOTOCOGRAPHY DATA USING EXTREME GRADIENT BOOSTING WITH ISOLATION FOREST AS OUTLIER DETECTION

Premature birth and birth defects contribute significantly to infant mortality, highlighting the need for early identification of fetal health risks. This study uses XGBoost for fetal health classification, integrating IForest for outlier detection to improve model performance. By varying the contam...

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Bibliographic Details

Published in:	Barekeng
Main Authors:	Firda Yunita Sari, Dian Candra Rini Novitasari, Abdulloh Hamid, Dina Zatusiva Haq
Format:	Article
Language:	English
Published:	Universitas Pattimura 2025-07-01
Subjects:	classification extreme gradient boosting (xgboost) fetal health isolation forest (iforest) outlier detection
Online Access:	https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/16795