Open AccessCombining Resampling Strategies and Ensemble Machine Learning Methods to Enhance Prediction of Neonates with a Low Apgar Score After Induction of Labor in Northern Tanzania
Author(s) -
Clifford Silver Tarimo,
Soumitra S. Bhuyan,
Quanman Li,
Weicun Ren,
Michael Johnson Mahande,
Jian Wu
Publication year - 2021
Publication title -
risk management and healthcare policy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.828
H-Index - 22
ISSN - 1179-1594
DOI - 10.2147/rmhp.s331077
Subject(s) - boosting (machine learning) , gradient boosting , machine learning , artificial intelligence , adaboost , receiver operating characteristic , computer science , resampling , random forest , apgar score , ensemble learning , medicine , classifier (uml) , birth weight , pregnancy , biology , genetics
The goal of this study was to establish the most efficient boosting method in predicting neonatal low Apgar scores following labor induction intervention and to assess whether resampling strategies would improve the predictive performance of the selected boosting algorithms.