Predictive Analytics Models for Identifying Maternal Mortality Risk Factors in National Health Datasets

Maternal mortality remains one of the most pressing public health challenges worldwide, disproportionately affecting low- and middle-income countries where health system capacity and access to care are limited. Identifying and addressing the risk factors associated with maternal deaths is critical for achieving global health targets, including the Sustainable Development Goals. Traditional methods of analyzing maternal health data have provided valuable insights but are often constrained by limited scalability, delayed analysis, and the inability to capture complex interactions among diverse risk factors. Predictive analytics models, drawing from machine learning, statistical modeling, and data mining techniques, present a promising approach to overcome these limitations by leveraging large-scale national health datasets to identify high-risk populations with greater accuracy and timeliness. These models can integrate multidimensional data, including demographic, clinical, socioeconomic, and environmental variables, to uncover patterns that may not be immediately apparent through conventional methods. Techniques such as logistic regression, random forests, gradient boosting, and neural networks have shown potential in predicting maternal mortality by capturing both linear and nonlinear associations. Furthermore, spatiotemporal modeling and clustering approaches allow for the identification of geographic disparities and community-level determinants of maternal health risks. By generating predictive insights, these models can guide policymakers, health systems, and frontline workers in targeting interventions, optimizing resource allocation, and designing preventive strategies tailored to vulnerable groups. The application of predictive analytics to maternal mortality surveillance not only enhances early risk detection but also supports evidence-based decision-making for maternal health programs. However, challenges remain, including data quality, ethical considerations regarding sensitive health information, and the need for model interpretability to ensure clinical and policy relevance. This study underscores the importance of integrating predictive analytics into national maternal health monitoring systems as a transformative step toward reducing preventable maternal deaths and improving equity in maternal healthcare outcomes.