Predicting skilled delivery service use in Ethiopia: dual application of logistic regression and machine learning algorithms.

Journal: BMC medical informatics and decision making
PMID: 31690306

Abstract

BACKGROUND: Skilled assistance during childbirth is essential to reduce maternal deaths. However, in Ethiopia, which is among the six countries contributing to more than half of the global maternal deaths, the coverage of births attended by skilled health personnel remains very low. The aim of this study was to identify determinants and develop a predictive model for skilled delivery service use in Ethiopia by applying logistic regression and machine-learning techniques.

Authors

  • Brook Tesfaye
    World Health Organization, Kenya Country Representative Office, United Nations Office in Nairobi (UNON), Gigiri Complex, Block "U", Nairobi, Kenya. balagerusew7@gmail.com.
  • Suleman Atique
    Graduate Institute of Biomedical Informatics, Taipei Medical University, Taiwan.
  • Tariq Azim
    John Snow Incorporated (JSI) Research and Training Institute, Arlington, VA, USA.
  • Mihiretu M Kebede
    Leibniz Institute for Prevention Research and Epidemiology -BIPS, Achterstraße, 30, Bremen, Germany.

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