Predictive analysis on the factors associated with birth Outcomes: A machine learning perspective.

Journal: International journal of medical informatics

PMID: 38905958

Abstract

BACKGROUND: Recent studies reveal that around 1.9 million stillbirths occur annually worldwide, with Sub-Saharan Africa having among the highest cases. Some Sub-Saharan African countries, including Ghana, failed to meet Millennium Development Goal 5 (MDG5) by 2015 and may struggle to meet Sustainable Development Goal 3 (SDG3) despite maternal healthcare interventions. Concerns arise about Ghana's ability to achieve the World Health Organization's neonatal mortality goal of 12 per 1000 live births by 2030. This study aims to identify key factors influencing childbirth outcomes and create a predictive method for high-risk pregnancies.

Authors

Atinuke Olusola Adebanji

Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Clement Asare

Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Samuel Asante Gyamerah

Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.

Keywords

Adult Female Gestational Age Ghana Humans Infant, Newborn Live Birth Logistic Models Machine Learning Neural Networks, Computer Parturition Pregnancy Pregnancy Outcome Stillbirth Young Adult

External Resources

View on PubMed Access via DOI PubMed (38905958)

Predictive analysis on the factors associated with birth Outcomes: A machine learning perspective.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals