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.

This study used ANCOVA models to investigate how pandemic characteristics—spreading speed, severity, and vaccination requirements—affect travel intentions. The results reveal that these factors explain 31.7% of the variance in travel decisions, with disease-spreading speed and severity being the most significant determinants. While vaccination requirements are relevant, they play a secondary role compared to the immediate threat of disease characteristics. The interaction effects between these factors further demonstrate their combined impact on travel reluctance. Demographic variables, such as gender and the presence of children, also influence decisions in specific contexts. These findings contribute to the understanding of risk perception during health crises, reinforcing the role of perceived severity in shaping cautious travel behavior. Practical implications for the tourism industry include the need for transparent communication, tailored health protocols, and demographic-specific marketing strategies. Future research should explore broader factors and adopt longitudinal approaches to capture evolving travel intentions during pandemics.