This study investigates the effectiveness of machine learning (ML) in predicting emergency obstetric emergencies in Sub-Saharan Africa to improve maternal health outcomes. By examining the relevant literature, the study highlights issues that impede efficient decision-making and interventions, such as a lack of high-quality healthcare data. While machine learning models such as logistic regression, decision trees, support vector machines, neural networks, and random forests can achieve high accuracy in controlled environments, they face practical challenges, including inconsistent data quality, limited access to technology, and a shortage of trained personnel. For ML to be implemented equitably, ethical factors such as algorithmic bias and data privacy are essential. The transformative potential of machine learning in emergency obstetric care is highlighted by its benefits in early detection, individualized care, resource management, and data-driven decision-making. To fully reap these advantages, however, implementation issues and data quality must be resolved. The rapid expansion of biomedical data calls for innovative approaches to help healthcare professionals effectively analyse large datasets and reach well-informed conclusions. To maximize resource allocation, enhance patient care, and continually improve clinical outcomes, future research should focus on developing novel machine learning algorithms, improving data integration and interoperability, and fostering a data-driven culture. This study investigates the effectiveness of machine learning (ML) in predicting emergency obstetric emergencies in Sub-Saharan Africa to improve maternal health outcomes. By examining the relevant literature, the study highlights issues that impede efficient decision-making and interventions, such as a lack of high-quality healthcare data. While machine learning models such as logistic regression, decision trees, support vector machines, neural networks, and random forests can achieve high accuracy in controlled environments, they face practical challenges, including inconsistent data quality, limited access to technology, and a shortage of trained personnel. For ML to be implemented equitably, ethical factors such as algorithmic bias and data privacy are essential. The transformative potential of machine learning in emergency obstetric care is highlighted by its benefits in early detection, individualized care, resource management, and data-driven decision-making. To fully reap these advantages, however, implementation issues and data quality must be resolved. The rapid expansion of biomedical data calls for innovative approaches to help healthcare professionals effectively analyse large datasets and reach well-informed conclusions. To maximize resource allocation, enhance patient care, and continually improve clinical outcomes, future research should focus on developing novel machine learning algorithms, improving data integration and interoperability, and fostering a data-driven culture.
Igbokwe et al. (Mon,) studied this question.