According to the World Health Organization, maternal mortality rates remain a critical public health issue, with 94% of maternal deaths occurring in low- and middle-income countries (LMICs), where the rates reached 430 per 100,000 live births in 2020 compared to 13 in high-income countries. Despite this difference, only a few studies have investigated whether sparse data and features such as vital signs can effectively predict maternal health risks. This study addresses this gap by evaluating the predictive capability of vital sign data using machine learning models trained on a dataset of 1014 pregnant women from rural Bangladesh. This study developed multiple machine learning models using a dataset containing age, blood pressure, temperature, heart rate, and blood glucose of 1014 pregnant women from rural Bangladesh. The models’ performance were evaluated using regular, random and stratified sampling techniques. Additionally, we developed a stacking ensemble machine learning model combining multiple methods to evaluate predictive accuracy. A key contribution of this study is developing a stacking ensemble model combined with stratified sampling, an approach not previously considered in maternal health risk prediction. The ensemble model using stratified sampling achieved the highest accuracy (87.2%), outperforming CatBoost (84.7%), XGBoost (84.2%), random forest (81.3%) and decision trees (80.3%) without stratified sampling. Observations from our study demonstrate the feasibility of using sparse data and features for maternal health risk prediction using algorithms. By focusing on data from resource-constrained settings, we show that machine learning offers a convenient and accessible solution to improve prenatal care and reduce maternal deaths in LMICs.

In today's data-driven world, organizations across industries are grappling with vast amounts of data, and the realm of market intelligence is no exception. The explosive growth of big data has created both opportunities and challenges for businesses seeking to gain valuable insights from their data assets. This chapter proposes to explore the significance of big data visualization with key performance indicator (KPI) dashboards as a powerful technique to visualize and extract actionable intelligence from complex datasets in the context of market intelligence. This chapter provides a comprehensive overview of KPI dashboards as these are essential tools for monitoring and analyzing business performance and to make data-driven decisions. The chapter explores the importance of KPIs, the design and implementation of KPI dashboards in big data. Additionally, it discusses the benefits and challenges associated with these tools, along with real-world examples of their successful implementation. Request access from your librarian to read this chapter's full text.