A hybrid boosting ensemble model for predicting maternal mortality and sustaining reproductive

Abstract

A successful pregnancy is a contingent upon a complex network of interdependent biological adaptations, including maternal immune responses and hormonal balance. Recent improvements in high-intelligence technology have enabled the combination of clinical and social data with multi-omics biological data can offer the opportunity to detect maternal risk during pregnancy. The United Nations' sustainable development goal (SDG 3) aims to improve maternal health and reduce child and maternal mortality by 2030. Nevertheless, maternal mortality has not decreased at the indicated rate, especially in developing countries like Ghana. This paper aims to establish an intelligent machine learning-based system for effectively monitoring and predicting pregnant women's risk levels. We assessed pregnant women's health data and risk variables to determine the maternal risk intensity level during pregnancy. Therefore, we proposed a hybrid ensemble algorithm (XGBoost and CatBoost) to determine the significant health factors associated with maternal health and predict the mother's risk level during pregnancy. The study outcome showed that blood sugar, age and body temperature were the most significant factors in determining the risk level of a pregnant woman in MM. Also, the prediction outcome (accuracy of 93.99%, AUC of 96.96%, recall 92.44%, and precision 93.46%) shows that our model performed well compared with other studies and machine learning algorithms.