Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania.

Journal: BMC pregnancy and childbirth
PMID: 35365129

Abstract

BACKGROUND: Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes. We set out to investigate important attributes and train popular machine learning (ML) algorithms to correctly classify neonates with a low Apgar scores from an imbalanced learning perspective.

Authors

  • Clifford Silver Tarimo
    College of Public Health, Zhengzhou University, Zhengzhou, China.
  • Soumitra S Bhuyan
    School of Planning and Public Policy, Rutgers University-New Brunswick, New York, New York, USA.
  • Yizhen Zhao
    Luoyang Orthopedic Traumatological Hospital of Henan Province, Luoyang, China.
  • Weicun Ren
    Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001, Henan, China.
  • Akram Mohammed
    Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America.
  • Quanman Li
    College of Public Health, Zhengzhou University, Zhengzhou, China.
  • Marilyn Gardner
    Department of Public Health, Western Kentucky University, 1906 College Heights Blvd, Bowling Green, KY, 42101, USA.
  • Michael Johnson Mahande
    Institute of Public Health, Kilimanjaro Christian Medical University College, P.O. Box 2240, Moshi, Tanzania.
  • Yuhui Wang
    School of Accounting, Harbin University of Commerce, Harbin 150028, Heilongjiang, China.
  • Jian Wu
    Department of Medical Technology, Jiangxi Medical College, Shangrao, Jiangxi, China.

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