A Machine Learning Approach for Predicting Maternal Health Risks in Lower-Middle-Income Countries Using Sparse Data and Vital Signs

Avnish Malde, Vishnunarayan Girishan Prabhu (), Dishant Banga, Michael Hsieh, Chaithanya Renduchintala and Ronald Pirrallo
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Avnish Malde: Department of Industrial Engineering, Clemson University, Clemson, SC 29634, USA
Vishnunarayan Girishan Prabhu: School of Modeling, Simulation, and Training, University of Central Florida, Orlando, FL 32827, USA
Dishant Banga: Department of Industrial and Systems Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Michael Hsieh: College of Medicine, University of Central Florida, Orlando, FL 32827, USA
Chaithanya Renduchintala: Rosen College of Hospitality Management, University of Central Florida, Orlando, FL 32827, USA
Ronald Pirrallo: Department of Emergency Medicine, Prisma Health Upstate, Greenville, SC 29605, USA

Future Internet, 2025, vol. 17, issue 5, 1-17

Abstract: 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.

Keywords: maternal risk prediction; risk assessment; vital signs; low- and middle-income countries; health equity (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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